Showing 25 of 322 results
The first evidence for the Higgs boson decay to a $Z$ boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision data sets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb$^{-1}$ for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is $2.2\pm0.7$ times the Standard Model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.
The negative profile log-likelihood test statistic, where $\Lambda$ represents the likelihood ratio, as a function of the signal strength $\mu$ derived from the ATLAS data, the CMS data, and the combined result.
Measurements of the suppression and correlations of dijets is performed using 3 $\mu$b$^{-1}$ of Xe+Xe data at $\sqrt{s_{\mathrm{NN}}} = 5.44$ TeV collected with the ATLAS detector at the LHC. Dijets with jets reconstructed using the $R=0.4$ anti-$k_t$ algorithm are measured differentially in jet $p_{\text{T}}$ over the range of 32 GeV to 398 GeV and the centrality of the collisions. Significant dijet momentum imbalance is found in the most central Xe+Xe collisions, which decreases in more peripheral collisions. Results from the measurement of per-pair normalized and absolutely normalized dijet $p_{\text{T}}$ balance are compared with previous Pb+Pb measurements at $\sqrt{s_{\mathrm{NN}}} =5.02$ TeV. The differences between the dijet suppression in Xe+Xe and Pb+Pb are further quantified by the ratio of pair nuclear-modification factors. The results are found to be consistent with those measured in Pb+Pb data when compared in classes of the same event activity and when taking into account the difference between the center-of-mass energies of the initial parton scattering process in Xe+Xe and Pb+Pb collisions. These results should provide input for a better understanding of the role of energy density, system size, path length, and fluctuations in the parton energy loss.
The centrality intervals in Xe+Xe collisions and their corresponding TAA with absolute uncertainties.
The centrality intervals in Xe+Xe and Pb+Pb collisions for matching SUM ET FCAL intervals and respective TAA values for Xe+Xe collisions.
The performance of the jet energy scale (JES) for jets with $|y| < 2.1$ evaluated as a function of pT_truth in different centrality bins. Simulated hard scatter events were overlaid onto events from a dedicated sample of minimum-bias Xe+Xe data.
The performance of jet energy resolution (JER) for jets with |y| < 2.1 evaluated as a function of pT_truth in different centrality bins. Simulated hard scatter events were overlaid onto events from a dedicated sample of minimum-bias Xe+Xe data. The fit parameters are listed in a sperate table (Extras 1)
The relative magnitude of systematic uncertainties for per-pair normalized xJ distributions in 0-10% Xe+Xe centrality
The relative magnitude of systematic uncertainties for absolutely normalized xJ distributions in 0-10% Xe+Xe centrality
The relative magnitude of systematic uncertainties for rho distributions for leading jets in 0-10% Xe+Xe centrality
Per-pair normalized xJ distribution evaluated in four centrality intervals and given pT1 interval.
Per-pair normalized xJ distribution evaluated in four centrality intervals and given pT1 interval.
Per-pair normalized xJ distribution evaluated in four centrality intervals and given pT1 interval.
Absolutely normalized xJ distribution evaluated in four centrality intervals and given pT1 interval.
Absolutely normalized xJ distribution evaluated in four centrality intervals and given pT1 interval.
Absolutely normalized xJ distribution evaluated in four centrality intervals and given pT1 interval.
Per-pair normalized xJ distribution in Xe+Xe collisions.
Per-pair normalized xJ distribution in Pb+Pb collisions.
Per-pair normalized xJ distribution in Xe+Xe collisions.
Per-pair normalized xJ distribution in Pb+Pb collisions.
Per-pair normalized xJ distribution in Xe+Xe collisions.
Per-pair normalized xJ distribution in Pb+Pb collisions.
Absolutely normalized xJ distribution in Xe+Xe collisions.
Absolutely normalized xJ distribution in Xe+Xe collisions.
Absolutely normalized xJ distribution in Xe+Xe collisions.
Absolutely normalized xJ distribution in Xe+Xe collisions.
Absolutely normalized xJ distribution in Xe+Xe collisions.
Absolutely normalized xJ distribution in Xe+Xe collisions.
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of leading jet pT in the same centrality intervals.
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of leading jet pT in the same centrality intervals.
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of leading jet pT in the same centrality intervals.
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of subleading jet pT in the same centrality intervals.
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of subleading jet pT in the same centrality intervals.
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of subleading jet pT in the same centrality intervals.
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of leading jet pT in the same SUM ETFCal intervals (selecting equivalent event activity)
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of leading jet pT in the same SUM ETFCal intervals (selecting equivalent event activity)
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of leading jet pT in the same SUM ETFCal intervals (selecting equivalent event activity)
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of subleading jet pT in the same SUM ETFCal intervals (selecting equivalent event activity)
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of subleading jet pT in the same SUM ETFCal intervals (selecting equivalent event activity)
The ratios of Xe+Xe and Pb+Pb pair nuclear-modification factors, rho, evaluated as a function of subleading jet pT in the same SUM ETFCal intervals (selecting equivalent event activity)
Parameter a,b, and c from JER fits in Figure 1b.
Per-pair normalized xJ distribution in Xe+Xe collisions for selected Pb+Pb centrality and pT1 bin.
Per-pair normalized xJ distribution in Xe+Xe collisions for selected Pb+Pb centrality and pT1 bin.
Per-pair normalized xJ distribution in Xe+Xe collisions for selected Pb+Pb centrality and pT1 bin.
Per-pair normalized xJ distribution in Xe+Xe collisions for selected Pb+Pb centrality and pT1 bin.
Per-pair normalized xJ distribution in Xe+Xe collisions for selected Pb+Pb centrality and pT1 bin.
Per-pair normalized xJ distribution in Xe+Xe collisions for selected Pb+Pb centrality and pT1 bin.
Per-pair normalized xJ distribution in Xe+Xe collisions for selected Pb+Pb centrality and pT1 bin.
Per-pair normalized xJ distribution in Xe+Xe collisions for selected Pb+Pb centrality and pT1 bin.
Per-pair normalized xJ distribution in Xe+Xe collisions for selected Pb+Pb centrality and pT1 bin.
A search for flavor-changing neutral-current couplings between a top quark, an up or charm quark and a $Z$ boson is presented, using proton-proton collision data at $\sqrt{s} = 13$ TeV collected by the ATLAS detector at the Large Hadron Collider. The analyzed dataset corresponds to an integrated luminosity of 139 fb$^{-1}$. The search targets both single-top-quark events produced as $gq\rightarrow tZ$ (with $q = u, c$) and top-quark-pair events, with one top quark decaying through the $t \rightarrow Zq$ channel. The analysis considers events with three leptons (electrons or muons), a $b$-tagged jet, possible additional jets, and missing transverse momentum. The data are found to be consistent with the background-only hypothesis and 95% confidence-level limits on the $t \rightarrow Zq$ branching ratios are set, assuming only tensor operators of the Standard Model effective field theory framework contribute to the $tZq$ vertices. These are $6.2 \times 10^{-5}$ ($13\times 10^{-5}$) for $t\rightarrow Zu$ ($t\rightarrow Zc$) for a left-handed $tZq$ coupling, and $6.6 \times 10^{-5}$ ($12\times 10^{-5}$) in the case of a right-handed coupling. These results are interpreted as 95% CL upper limits on the strength of corresponding couplings, yielding limits for $|C_{uW}^{(13)*}|$ and $|C_{uB}^{(13)*}|$ ($|C_{uW}^{(31)}|$ and $|C_{uB}^{(31)}|$) of 0.15 (0.16), and limits for $|C_{uW}^{(23)*}|$ and $|C_{uB}^{(23)*}|$ ($|C_{uW}^{(32)}|$ and $|C_{uB}^{(32)}|$) of 0.22 (0.21), assuming a new-physics energy scale $\Lambda_\text{NP}$ of 1 TeV.
Summary of the signal strength $\mu$ parameters obtained from the fits to extract LH and RH results for the FCNC tZu and tZc couplings. For the reference branching ratio, the most stringent limits are used.
Observed and expected 95% CL limits on the FCNC $t\rightarrow Zq$ branching ratios and the effective coupling strengths for different vertices and couplings (top eight rows). For the latter, the energy scale is assumed to be $\Lambda_{NP}$ = 1 TeV. The bottom rows show, for the case of the FCNC $t\rightarrow Zu$ branching ratio, the observed and expected 95% CL limits when only one of the two SRs, either SR1 or SR2, and all CRs are included in the likelihood.
Comparison between data and background prediction before the fit (Pre-Fit) for the mass of the SM top-quark candidate in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction before the fit (Pre-Fit) for the mass of the FCNC top-quark candidate in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction before the fit (Pre-Fit) for the mass of the SM top-quark candidate in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction before the fit (Pre-Fit) for the transverse momentum of the Z boson candidate in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The four FCNC LH signals are also shown separately, normalized to five times the cross-section corresponding to the most stringent observed branching ratio limits. The first (last) bin in all distributions includes the underflow (overflow). The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the mass sideband CR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{2}^{u}$ discriminant in the mass sideband CR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{2}^{u}$ discriminant in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the mass sideband CR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{2}^{c}$ discriminant in the mass sideband CR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 500 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{1}$ discriminant in SR1. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZc LH coupling extraction. The distribution is for the $D_{2}^{c}$ discriminant in SR2. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZc LH signals are also separately shown, normalized to 50 times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the leading lepton $p_{T}$ in the $t\bar{t}$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the third lepton $p_{T}$ in the $t\bar{t}$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the leading lepton $p_{T}$ in the $t\bar{t}Z$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The last bin includes the overflow. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Comparison between data and background prediction after the fit to data (Post-Fit) for the FCNC tZu LH coupling extraction. The distribution is for the $D_{1}$ discriminant in the $t\bar{t}Z$ CR. The uncertainty band includes both the statistical and systematic uncertainties in the background prediction. The FCNC tZu LH signals are also shown separately, normalized to $10^{3}$ times the best fit of the signal yield. The lower panels show the ratios of the data (Data) to the background prediction (Bkg.).
Several extensions of the Standard Model predict the production of dark matter particles at the LHC. A search for dark matter particles produced in association with a dark Higgs boson decaying into $W^{+}W^{-}$ in the $\ell^\pm\nu q \bar q'$ final states with $\ell=e,\mu$ is presented. This analysis uses 139 fb$^{-1}$ of $pp$ collisions recorded by the ATLAS detector at a centre-of-mass energy of 13 TeV. The $W^\pm \to q\bar q'$ decays are reconstructed from pairs of calorimeter-measured jets or from track-assisted reclustered jets, a technique aimed at resolving the dense topology from a pair of boosted quarks using jets in the calorimeter and tracking information. The observed data are found to agree with Standard Model predictions. Scenarios with dark Higgs boson masses ranging between 140 and 390 GeV are excluded.
Probability of finding at least one TAR jet, where the p<sub>T</sub>-leading TAR jet passes the m<sub>Wcand</sub> and D<sub>2</sub><sup>β=1</sup> requirements, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=500 GeV, with the preselections applied.
Probability of finding at least one TAR jet, where the p<sub>T</sub>-leading TAR jet passes the m<sub>Wcand</sub> and D<sub>2</sub><sup>β=1</sup> requirements, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=1000 GeV, with the preselections applied.
Probability of finding at least one TAR jet, where the p<sub>T</sub>-leading TAR jet passes the m<sub>Wcand</sub> and D<sub>2</sub><sup>β=1</sup> requirements, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=1700 GeV, with the preselections applied.
Probability of finding at least one TAR jet, where the p<sub>T</sub>-leading TAR jet passes the m<sub>Wcand</sub> and D<sub>2</sub><sup>β=1</sup> requirements, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=2100 GeV, with the preselections applied.
Probability of finding a W<sub>had</sub> candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=500 GeV, with the preselections applied that do not pass the requirements of the merged category.
Probability of finding a W<sub>had</sub> candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=1000 GeV, with the preselections applied that do not pass the requirements of the merged category.
Probability of finding a W<sub>had</sub> candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=1700 GeV, with the preselections applied that do not pass the requirements of the merged category.
Probability of finding a W<sub>had</sub> candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m<sub>s</sub>. The probability is determined in a sample of signal events with m<sub>Z'</sub>=2100 GeV, with the preselections applied that do not pass the requirements of the merged category.
Observed exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected+1σ exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected-1σ exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected+2σ exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Expected-2σ exclusion contour at 95% C.L. for the dark Higgs model in the (m<sub>Z'</sub>, m<sub>s</sub>) plane for g<sub>q</sub>=0.25, g<sub>χ</sub>=1, m<sub>χ</sub>=200 GeV, and sinθ=0.01.
Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) for m<sub>Z'</sub>=0.5 TeV as a function of m<sub>s</sub>. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) process for m<sub>Z'</sub>=0.5 TeV, shown in dashed blue.
Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) for m<sub>Z'</sub>=1 TeV as a function of m<sub>s</sub>. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) process for m<sub>Z'</sub>=1 TeV, shown in dashed blue.
Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) for m<sub>Z'</sub>=1.7 TeV as a function of m<sub>s</sub>. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) process for m<sub>Z'</sub>=1.7 TeV, shown in dashed blue.
Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) for m<sub>Z'</sub>=2.1 TeV as a function of m<sub>s</sub>. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W<sup>±</sup> W<sup>∓</sup>) process for m<sub>Z'</sub>=2.1 TeV, shown in dashed blue.
Data overlaid on SM background yields stacked in each SR and CR category after the fit to data ('Post-fit'). The yields in the SR are broken down into their contributions to the individual bins. The maximum-likelihood estimators are set to the conditional values of the CR-only fit, and propagated to SR and CRs.
Dominant sources of uncertainty for three dark Higgs scenarios after the fit to data. The uncertainties are quantified in terms of their contribution to the fitted signal uncertainty that is expressed relative to the theory prediction. Three representative dark Higgs signal scenarios with g<sub>q</sub>=0.25, g<sub>χ</sub>=1.0, sinθ=0.01 and m<sub>χ</sub>=200 GeV are considered, which are indicated using the (m<sub>Z'</sub>, m<sub>s</sub>) format in units of GeV in the table columns.
Cumulative efficiencies in the merged category for three representative dark Higgs signal scenarios with g<sub>q</sub>=0.25, g<sub>&chi</sub>;=1.0, sinθ=0.01, m<sub>Z'</sub> = 1 TeV, and m<sub>χ</sub>=200 GeV considering s→W(ℓν)W(qq) decays only.
Cumulative efficiencies in the resolved category for three representative dark Higgs signal scenarios with g<sub>q</sub>=0.25, g<sub>&chi</sub>;=1.0, sinθ=0.01, m<sub>Z'</sub> = 1 TeV, and m<sub>χ</sub>=200 GeV considering s→W(ℓν)W(qq) decays only.
Theoretical cross section for σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) for each of the dark Higgs signal points at m<sub>Z′</sub> ={300, 350, 400, 500, 750} GeV, with g<sub>q</sub> = 0.25, g<sub>χ = 1.0, sinθ = 0.01, m<sub>Z′</sub> = 1 TeV , and m<sub>χ</sub> = 200 GeV. Also shown are experimentally excluded cross sections of σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (±1σ).
Theoretical cross section for σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) for each of the dark Higgs signal points at m<sub>Z′</sub> ={1000, 1700} GeV, with g<sub>q</sub> = 0.25, g<sub>χ = 1.0, sinθ = 0.01, m<sub>Z′</sub> = 1 TeV , and m<sub>χ</sub> = 200 GeV. Also shown are experimentally excluded cross sections of σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (±1σ).
Theoretical cross section for σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) for each of the dark Higgs signal points at m<sub>Z′</sub> ={2100, 2500, 2900, 3300} GeV, with g<sub>q</sub> = 0.25, g<sub>χ = 1.0, sinθ = 0.01, m<sub>Z′</sub> = 1 TeV , and m<sub>χ</sub> = 200 GeV. Also shown are experimentally excluded cross sections of σ(pp → sχχ) × B(s → W<sup>±</sup>W<sup>∓</sup>) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (±1σ).
Inclusive and differential measurements of the top-antitop ($t\bar{t}$) charge asymmetry $A_\text{C}^{t\bar{t}}$ and the leptonic asymmetry $A_\text{C}^{\ell\bar{\ell}}$ are presented in proton-proton collisions at $\sqrt{s} = 13$ TeV recorded by the ATLAS experiment at the CERN Large Hadron Collider. The measurement uses the complete Run 2 dataset, corresponding to an integrated luminosity of 139 fb$^{-1}$, combines data in the single-lepton and dilepton channels, and employs reconstruction techniques adapted to both the resolved and boosted topologies. A Bayesian unfolding procedure is performed to correct for detector resolution and acceptance effects. The combined inclusive $t\bar{t}$ charge asymmetry is measured to be $A_\text{C}^{t\bar{t}} = 0.0068 \pm 0.0015$, which differs from zero by 4.7 standard deviations. Differential measurements are performed as a function of the invariant mass, transverse momentum and longitudinal boost of the $t\bar{t}$ system. Both the inclusive and differential measurements are found to be compatible with the Standard Model predictions, at next-to-next-to-leading order in quantum chromodynamics perturbation theory with next-to-leading-order electroweak corrections. The measurements are interpreted in the framework of the Standard Model effective field theory, placing competitive bounds on several Wilson coefficients.
- - - - - - - - Overview of HEPData Record - - - - - - - - <br/><br/> <b>Results:</b> <ul> <li><a href="132116?version=1&table=Resultsforchargeasymmetryinclusive">$A_C^{t\bar{t}}$</a> <li><a href="132116?version=1&table=Resultsforchargeasymmetryvsmtt">$A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$</a> <li><a href="132116?version=1&table=Resultsforchargeasymmetryvspttt">$A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$</a> <li><a href="132116?version=1&table=Resultsforchargeasymmetryvsbetatt">$A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$</a> <li><a href="132116?version=1&table=Resultsforleptonicchargeasymmetryinclusive">$A_C^{\ell\bar{\ell}}$</a> <li><a href="132116?version=1&table=Resultsforchargeasymmetryvsllmll">$A_C^{\ell\bar{\ell}}$ vs $m_{\ell\bar{\ell}}$</a> <li><a href="132116?version=1&table=Resultsforchargeasymmetryvsllptll">$A_C^{\ell\bar{\ell}}$ vs $p_{T,\ell\bar{\ell}}$</a> <li><a href="132116?version=1&table=Resultsforchargeasymmetryvsllbetall">$A_C^{\ell\bar{\ell}}$ vs $\beta_{z,\ell\bar{\ell}}$</a> </ul> <b>Bounds on the Wilson coefficients:</b> <ul> <li><a href="132116?version=1&table=BoundsonWilsoncoefficientschargeasymmetryinclusive">$A_C^{t\bar{t}}$</a> <li><a href="132116?version=1&table=BoundsonWilsoncoefficientschargeasymmetryvsmtt">$A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$</a> </ul> <b>Ranking of systematic uncertainties:</b></br> Inclusive:<a href="132116?version=1&table=NPrankingchargeasymmetryinclusive">$A_C^{t\bar{t}}$</a></br> <b>$A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$:</b> <ul> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsbetattbin0">$\beta_{z,t\bar{t}} \in[0,0.3]$</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsbetattbin1">$\beta_{z,t\bar{t}} \in[0.3,0.6]$</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsbetattbin2">$\beta_{z,t\bar{t}} \in[0.6,0.8]$</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsbetattbin3">$\beta_{z,t\bar{t}} \in[0.8,1]$</a> </ul> <b>$A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$:</b> <ul> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsmttbin0">$m_{t\bar{t}}$ < $500$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsmttbin1">$m_{t\bar{t}} \in [500,750]$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsmttbin2">$m_{t\bar{t}} \in [750,1000]$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsmttbin3">$m_{t\bar{t}} \in [1000,1500]$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsmttbin4">$m_{t\bar{t}}$ > $1500$GeV</a> </ul> <b>$A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$:</b> <ul> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsptttbin0">$p_{T,t\bar{t}} \in [0,30]$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsptttbin1">$p_{T,t\bar{t}} \in[30,120]$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsptttbin2">$p_{T,t\bar{t}}$ > $120$GeV</a> </ul> Inclusive leptonic:<a href="132116?version=1&table=NPrankingleptonicchargeasymmetryinclusive">$A_C^{\ell\bar{\ell}}$</a></br> <b>$A_C^{\ell\bar{\ell}}$ vs $\beta_{z,\ell\bar{\ell}}$</b> <ul> <li><a href="132116?version=1&tableNPrankingchargeasymmetry=vsllbetallbin0">$\beta_{z,\ell\bar{\ell}} \in [0,0.3]$</a> <li><a href="132116?version=1&tableNPrankingchargeasymmetry=vsllbetallbin1">$\beta_{z,\ell\bar{\ell}} \in [0.3,0.6]$</a> <li><a href="132116?version=1&tableNPrankingchargeasymmetry=vsllbetallbin2">$\beta_{z,\ell\bar{\ell}} \in [0.6,0.8]$</a> <li><a href="132116?version=1&tableNPrankingchargeasymmetry=vsllbetallbin3">$\beta_{z,\ell\bar{\ell}} \in [0.8,1]$</a> </ul> <b>$A_C^{\ell\bar{\ell}}$ vs $m_{\ell\bar{\ell}}$</b> <ul> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsllmllbin0">$m_{\ell\bar{\ell}}$ < $200$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsllmllbin1">$m_{\ell\bar{\ell}} \in [200,300]$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsllmllbin2">$m_{\ell\bar{\ell}} \in [300,400]$Ge$</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsllmllbin3">$m_{\ell\bar{\ell}}$ > $400$GeV</a> </ul> <b>$A_C^{\ell\bar{\ell}}$ vs $p_{T,\ell\bar{\ell}}$</b> <ul> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsllptllbin0">$p_{T,\ell\bar{\ell}}\in [0,20]$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsllptllbin1">$p_{T,\ell\bar{\ell}}\in[20,70]$GeV</a> <li><a href="132116?version=1&table=NPrankingchargeasymmetryvsllptllbin2">$p_{T,\ell\bar{\ell}}$ > $70$GeV</a> </ul> <b>NP correlations:</b> <ul> <li><a href="132116?version=1&table=NPcorrelationschargeasymmetryinclusive">$A_C^{t\bar{t}}$</a> <li><a href="132116?version=1&table=NPcorrelationschargeasymmetryvsmtt">$A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$</a> <li><a href="132116?version=1&table=NPcorrelationschargeasymmetryvspttt">$A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$</a> <li><a href="132116?version=1&table=NPcorrelationschargeasymmetryvsbetatt">$A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$</a> <li><a href="132116?version=1&table=NPcorrelationsleptonicchargeasymmetryinclusive">$A_c^{\ell\bar{\ell}}$</a> <li><a href="132116?version=1&table=NPcorrelationschargeasymmetryvsllmll">$A_c^{\ell\bar{\ell}}$ vs $m_{\ell\bar{\ell}}$</a> <li><a href="132116?version=1&table=NPcorrelationschargeasymmetryvsllptll">$A_C^{\ell\bar{\ell}}$ vs $p_{T,\ell\bar{\ell}}$</a> <li><a href="132116?version=1&table=NPcorrelationschargeasymmetryvsllbetall">$A_C^{\ell\bar{\ell}}$ vs $\beta_{z,\ell\bar{\ell}}$</a> </ul> <b>Covariance matrices:</b> <ul> <li><a href="132116?version=1&table=Covariancematrixchargeasymmetryvsmtt">$A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$</a> <li><a href="132116?version=1&table=Covariancematrixchargeasymmetryvspttt">$A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$</a> <li><a href="132116?version=1&table=Covariancematrixchargeasymmetryvsbetatt">$A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$</a> <li><a href="132116?version=1&table=Covariancematrixleptonicchargeasymmetryvsllmll">$A_c^{\ell\bar{\ell}}$ vs $m_{\ell\bar{\ell}}$</a> <li><a href="132116?version=1&table=Covariancematrixleptonicchargeasymmetryvsllptll">$A_C^{\ell\bar{\ell}}$ vs $p_{T,\ell\bar{\ell}}$</a> <li><a href="132116?version=1&table=Covariancematrixleptonicchargeasymmetryvsllbetall">$A_C^{\ell\bar{\ell}}$ vs $\beta_{z,\ell\bar{\ell}}$</a> </ul>
The unfolded inclusive charge asymmetry. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed, and the impact of the linear term of the Wilson coefficient on the $A_C^{t\bar{t}}$ prediction is shown for two different values. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.
The unfolded differential charge asymmetry as a function of the invariant mass of the top pair system. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed, and the impact of the linear term of the Wilson coefficient on the $A_C^{t\bar{t}}$ prediction is shown for two different values. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.
The unfolded differential charge asymmetry as a function of the transverse momentum of the top pair system. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.
The unfolded differential charge asymmetry as a function of the longitudinal boost of the top pair system. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.
The unfolded inclusive leptonic asymmetry. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.
The unfolded differential leptonic asymmetry as a function of the invariant mass of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.
The unfolded differential leptonic asymmetry as a function of the transverse momentum of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.
The unfolded differential leptonic asymmetry as a function of the longitudinal boost of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.
Individual 68% and 95% CL bounds on the relevant Wilson coefficients of the SM Effective Field Theory in units of $\text{TeV}^{-2}$. The bounds are derived from the $A_C^{t\bar{t}}$ inclusive measurement. The experimental uncertainties are accounted for, in the form of the complete covariance matrix that keeps track of correlations between bins for the differential measurement. The theory uncertainty from the NNLO QCD + NLO EW calculation is included by explicitly varying the renormalization and factorization scales, or the parton density functions, in the calculation and registering the variations in the intervals.
Individual 68% and 95% CL bounds on the relevant Wilson coefficients of the SM Effective Field Theory in units of $\text{TeV}^{-2}$. The bounds are derived from the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement. The experimental uncertainties are accounted for, in the form of the complete covariance matrix that keeps track of correlations between bins for the differential measurement. The theory uncertainty from the NNLO QCD + NLO EW calculation is included by explicitly varying the renormalization and factorization scales, or the parton density functions, in the calculation and registering the variations in the intervals.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ inclusive measurement. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0,0.3]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.3,0.6]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.6,0.8]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.8,1]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ < 500 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [500,750] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [750,1000] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [1000,1500] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ > 1500 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ < 30 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ $\in$ [30,120] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ > 120 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ inclusive measurement. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0,0.3]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.3,0.6]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.6,0.8]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.8,1]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ < 200 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ $\in$ [200,300] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ $\in$ [300,400] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ > 400 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ < 20 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ $\in$ [20, 70] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ > 70 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.
Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ inclusive measurement. Only $|\rho_{ij}| > 0.05$ values are included.
Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.
Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.
Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.
Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ inclusive measurement. Only $|\rho_{ij}| > 0.05$ values are included.
Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.
Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.
Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.
Covariance matrix for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement.
Covariance matrix for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement.
Covariance matrix for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement.
Covariance matrix for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement.
Covariance matrix for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement.
Covariance matrix for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement.
A combination of measurements of the inclusive top-quark pair production cross-section performed by ATLAS and CMS in proton-proton collisions at centre-of-mass energies of 7 and 8 TeV at the LHC is presented. The cross-sections are obtained using top-quark pair decays with an opposite-charge electron-muon pair in the final state and with data corresponding to an integrated luminosity of about 5 fb$^{-1}$ at $\sqrt{s}=7$ TeV and about 20 fb$^{-1}$ at $\sqrt{s}=8$ TeV for each experiment. The combined cross-sections are determined to be $178.5 \pm 4.7$ pb at $\sqrt{s}=7$ TeV and $243.3^{+6.0}_{-5.9}$ pb at $\sqrt{s}=8$ TeV with a correlation of 0.41, using a reference top-quark mass value of 172.5 GeV. The ratio of the combined cross-sections is determined to be $R_{8/7}= 1.363\pm 0.032$. The combined measured cross-sections and their ratio agree well with theory calculations using several parton distribution function (PDF) sets. The values of the top-quark pole mass (with the strong coupling fixed at 0.118) and the strong coupling (with the top-quark pole mass fixed at 172.5 GeV) are extracted from the combined results by fitting a next-to-next-to-leading-order plus next-to-next-to-leading-log QCD prediction to the measurements. Using a version of the NNPDF3.1 PDF set containing no top-quark measurements, the results obtained are $m_t^\text{pole} = 173.4^{+1.8}_{-2.0}$ GeV and $\alpha_\text{s}(m_Z)= 0.1170^{+ 0.0021}_{-0.0018}$.
Full covariance matrix including all systematic uncertainties expressed as nuisance parameters. With the exception of the cross section parameters, all parameters were normalised to 1 before the fit. Therefore, the diagonal elements represent the constraint in quadrature.
Full covariance matrix including all systematic uncertainties expressed as nuisance parameters. With the exception of the cross section parameters, all parameters were normalised to 1 before the fit. Therefore, the diagonal elements represent the constraint in quadrature.
This paper presents studies of Bose-Einstein correlations (BEC) in proton-proton collisions at a centre-of-mass energy of 13 TeV, using data from the ATLAS detector at the CERN Large Hadron Collider. Data were collected in a special low-luminosity configuration with a minimum-bias trigger and a high-multiplicity track trigger, accumulating integrated luminosities of 151 $\mu$b$^{-1}$ and 8.4 nb$^{-1}$ respectively. The BEC are measured for pairs of like-sign charged particles, each with $|\eta|$ < 2.5, for two kinematic ranges: the first with particle $p_T$ > 100 MeV and the second with particle $p_T$ > 500 MeV. The BEC parameters, characterizing the source radius and particle correlation strength, are investigated as functions of charged-particle multiplicity (up to 300) and average transverse momentum of the pair (up to 1.5 GeV). The double-differential dependence on charged-particle multiplicity and average transverse momentum of the pair is also studied. The BEC radius is found to be independent of the charged-particle multiplicity for high charged-particle multiplicity (above 100), confirming a previous observation at lower energy. This saturation occurs independent of the transverse momentum of the pair.
Comparison of single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q) and C<sub>2</sub><sup>MC</sup>(Q), with the two-particle double-ratio correlation function, R<sub>2</sub>(Q), for the high-multiplicity track (HMT) events using the opposite hemisphere (OHP) like-charge particles pairs reference sample for k<sub>T</sub> - interval 1000 < k<sub>T</sub> ≤ 1500 MeV.
Comparison of single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q) and C<sub>2</sub><sup>MC</sup>(Q), with the two-particle double-ratio correlation function, R<sub>2</sub>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k<sub>T</sub> - interval 1000 < k<sub>T</sub> ≤ 1500 MeV.
The Bose-Einstein correlation (BEC) parameter R as a function of n<sub>ch</sub> for MB events using different MC generators in the calculation of R<sub>2</sub>(Q). The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.
The Bose-Einstein correlation (BEC) parameter R as a function of n<sub>ch</sub> for HMT events using different MC generators in the calculation of R<sub>2</sub>(Q). The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.
The Bose-Einstein correlation (BEC) parameter R as a function of k<sub>T</sub> for MB events using different MC generators in the calculation of R<sub>2</sub>(Q). The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.
The Bose-Einstein correlation (BEC) parameter λ as a function of k<sub>T</sub> for MB events using different MC generators in the calculation of R<sub>2</sub>(Q). The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.
The two-particle double-ratio correlation function, R<sub>2</sub>(Q), for pp collisions for track p<sub>T</sub> >100 MeV at √s=13 TeV in the multiplicity interval 71 ≤ n<sub>ch</sub> < 80 for the minimum-bias (MB) events. The blue dashed and red solid lines show the results of the exponential and Gaussian fits, respectively. The region excluded from the fits is shown. The statistical uncertainty and the systematic uncertainty for imperfections in the data reconstruction procedure are added in quadrature.
The two-particle double-ratio correlation function, R<sub>2</sub>(Q), for pp collisions for track p<sub>T</sub> >100 MeV at √s=13 TeV in the multiplicity interval 231 ≤ n<sub>ch</sub> < 300 for the high-multiplicity track (HMT) events. The blue dashed and red solid lines show the results of the exponential and Gaussian fits, respectively. The region excluded from the fits is shown. The statistical uncertainty and the systematic uncertainty for imperfections in the data reconstruction procedure are added in quadrature.
The dependence of the correlation strength, λ(m<sub>ch</sub>), on rescaled multiplicity, m<sub>ch</sub>, obtained from the exponential fit of the R<sub>2</sub>(Q) correlation functions for tracks with p<sub>T</sub> > 100 MeV and p<sub>T</sub> > 500 MeV at √s = 13 TeV for the minimum-bias (MB) and high multiplicity track (HMT) data. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the exponential fit of λ(m<sub>ch</sub>) for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively.
The dependence of the correlation strength, λ(m<sub>ch</sub>), on rescaled multiplicity, m<sub>ch</sub>, obtained from the exponential fit of the R<sub>2</sub>(Q) correlation functions for tracks with p<sub>T</sub> > 100 MeV and p<sub>T</sub> > 500 MeV at √s = 13 TeV for the minimum-bias (MB) and high multiplicity track (HMT) data. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the exponential fit of λ(m<sub>ch</sub>) for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively.
The dependence of the correlation strength, λ(m<sub>ch</sub>), on rescaled multiplicity, m<sub>ch</sub>, obtained from the exponential fit of the R<sub>2</sub>(Q) correlation functions for tracks with p<sub>T</sub> > 100 MeV and p<sub>T</sub> > 500 MeV at √s = 13 TeV for the minimum-bias (MB) and high multiplicity track (HMT) data. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the exponential fit of λ(m<sub>ch</sub>) for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively.
The dependence of the correlation strength, λ(m<sub>ch</sub>), on rescaled multiplicity, m<sub>ch</sub>, obtained from the exponential fit of the R<sub>2</sub>(Q) correlation functions for tracks with p<sub>T</sub> > 100 MeV and p<sub>T</sub> > 500 MeV at √s = 13 TeV for the minimum-bias (MB) and high multiplicity track (HMT) data. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the exponential fit of λ(m<sub>ch</sub>) for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively.
The dependence of the source radius, R(m<sub>ch</sub>), on m<sub>ch</sub>. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> < 1.2 for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m<sub>ch</sub> > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> > 1.45 with p<sub>T</sub> >100 MeV and for ∛m<sub>ch</sub> > 1.6 with p<sub>T</sub> >500 MeV, respectively.
The dependence of the source radius, R(m<sub>ch</sub>), on m<sub>ch</sub>. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> < 1.2 for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m<sub>ch</sub> > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> > 1.45 with p<sub>T</sub> >100 MeV and for ∛m<sub>ch</sub> > 1.6 with p<sub>T</sub> >500 MeV, respectively.
The dependence of the source radius, R(m<sub>ch</sub>), on m<sub>ch</sub>. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> < 1.2 for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m<sub>ch</sub> > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> > 1.45 with p<sub>T</sub> >100 MeV and for ∛m<sub>ch</sub> > 1.6 with p<sub>T</sub> >500 MeV, respectively.
The dependence of the source radius, R(m<sub>ch</sub>), on m<sub>ch</sub>. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> < 1.2 for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m<sub>ch</sub> > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> > 1.45 with p<sub>T</sub> >100 MeV and for ∛m<sub>ch</sub> > 1.6 with p<sub>T</sub> >500 MeV, respectively.
The dependence of the R(m<sub>ch</sub>) on ∛m<sub>ch</sub>. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> < 1.2 for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m<sub>ch</sub> > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> > 1.45 with p<sub>T</sub> >100 MeV and for ∛m<sub>ch</sub> > 1.6 with p<sub>T</sub> >500 MeV, respectively
The dependence of the R(m<sub>ch</sub>) on ∛m<sub>ch</sub>. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> < 1.2 for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m<sub>ch</sub> > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> > 1.45 with p<sub>T</sub> >100 MeV and for ∛m<sub>ch</sub> > 1.6 with p<sub>T</sub> >500 MeV, respectively
The dependence of the R(m<sub>ch</sub>) on ∛m<sub>ch</sub>. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> < 1.2 for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m<sub>ch</sub> > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> > 1.45 with p<sub>T</sub> >100 MeV and for ∛m<sub>ch</sub> > 1.6 with p<sub>T</sub> >500 MeV, respectively
The dependence of the R(m<sub>ch</sub>) on ∛m<sub>ch</sub>. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> < 1.2 for p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m<sub>ch</sub> > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m<sub>ch</sub>) for ∛m<sub>ch</sub> > 1.45 with p<sub>T</sub> >100 MeV and for ∛m<sub>ch</sub> > 1.6 with p<sub>T</sub> >500 MeV, respectively
Comparison of single-ratio two-particle correlation functions, using the unlike-charge particle (UCP) pair reference sample, for minimum-bias (MB) events, showing C<sub>2</sub><sup>data</sup>(Q) (top panel) at 13 TeV (black circles) and 7 TeV (open blue circles), and the ratio of C<sub>2</sub><sup>7 TeV</sup> (Q) to C<sub>2</sub><sup>13 TeV</sup> (Q) (bottom panel). Comparison of C<sub>2</sub><sup>data</sup> (Q) for representative multiplicity region 3.09 < m<sub>ch</sub> ≤ 3.86. The statistical and systematic uncertainties, combined in quadrature, are presented. The systematic uncertainties include track efficiency, Coulomb correction, non-closure and multiplicity-unfolding uncertainties.
Comparison of single-ratio two-particle correlation functions, using the unlike-charge particle (UCP) pair reference sample, for minimum-bias (MB) events, showing C<sub>2</sub><sup>data</sup>(Q) (top panel) at 13 TeV (black circles) and 7 TeV (open blue circles), and the ratio of C<sub>2</sub><sup>7 TeV</sup> (Q) to C<sub>2</sub><sup>13 TeV</sup> (Q) (bottom panel). Comparison of C<sub>2</sub><sup>data</sup> (Q) for representative k<sub>T</sub> region 400 < k<sub>T</sub> ≤500 MeV. The statistical and systematic uncertainties, combined in quadrature, are presented. The systematic uncertainties include track efficiency, Coulomb correction, non-closure and multiplicity-unfolding uncertainties.
The k<sub>T</sub> dependence of the correlation strength, λ(k<sub>T</sub>), obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions for events with multiplicity n<sub>ch</sub> ≥ 2 and transfer momentum of tracks with p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to λ(k<sub>T</sub>).
The k<sub>T</sub> dependence of the correlation strength, λ(k<sub>T</sub>), obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions for events with multiplicity n<sub>ch</sub> ≥ 2 and transfer momentum of tracks with p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to λ(k<sub>T</sub>).
The k<sub>T</sub> dependence of the correlation strength, λ(k<sub>T</sub>), obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions for events with multiplicity n<sub>ch</sub> ≥ 2 and transfer momentum of tracks with p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to λ(k<sub>T</sub>).
The k<sub>T</sub> dependence of the correlation strength, λ(k<sub>T</sub>), obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions for events with multiplicity n<sub>ch</sub> ≥ 2 and transfer momentum of tracks with p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to λ(k<sub>T</sub>).
The k<sub>T</sub> dependence of the source radius, R(k<sub>T</sub>), obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions for events with multiplicity n<sub>ch</sub> ≥ 2 and transfer momentum of tracks with p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to R(k<sub>T</sub>).
The k<sub>T</sub> dependence of the source radius, R(k<sub>T</sub>), obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions for events with multiplicity n<sub>ch</sub> ≥ 2 and transfer momentum of tracks with p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to R(k<sub>T</sub>).
The k<sub>T</sub> dependence of the source radius, R(k<sub>T</sub>), obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions for events with multiplicity n<sub>ch</sub> ≥ 2 and transfer momentum of tracks with p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to R(k<sub>T</sub>).
The k<sub>T</sub> dependence of the source radius, R(k<sub>T</sub>), obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions for events with multiplicity n<sub>ch</sub> ≥ 2 and transfer momentum of tracks with p<sub>T</sub> >100 MeV and p<sub>T</sub> >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to R(k<sub>T</sub>).
The two-dimensional dependence on m<sub>ch</sub> and k<sub>T</sub> for p<sub>T</sub> > 100 MeV for the correlation strength, λ, obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions using the MB sample for m<sub>ch</sub> ≤ 3.08 and the HMT sample for m<sub>ch</sub> > 3.08.
The two-dimensional dependence on m<sub>ch</sub> and k<sub>T</sub> for p<sub>T</sub> > 100 MeV for the source radius, R, obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions using the MB sample for m<sub>ch</sub> ≤ 3.08 and the HMT sample for m<sub>ch</sub> > 3.08.
The parameter λ for p<sub>T</sub> > 100 MeV as a function of k<sub>T</sub> in selected low m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 100 MeV as a function of k<sub>T</sub> in selected low m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 100 MeV as a function of k<sub>T</sub> in selected high m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 100 MeV as a function of k<sub>T</sub> in selected high m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 100 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.1 and 0.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 100 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.1 and 0.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 100 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 100 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 100 MeV as a function of k<sub>T</sub> in selected low m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 100 MeV as a function of k<sub>T</sub> in selected low m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 100 MeV as a function of k<sub>T</sub> in selected high m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 100 MeV as a function of k<sub>T</sub> in selected high m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 100 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.1 and 0.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 100 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.1 and 0.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 100 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 100 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The fit parameter μ describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter μ on m<sub>ch</sub> for tracks with p<sub>T</sub> >100 MeV (p<sub>T</sub> >500 MeV).
The fit parameter μ describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter μ on m<sub>ch</sub> for tracks with p<sub>T</sub> >100 MeV (p<sub>T</sub> >500 MeV).
The fit parameter μ describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter μ on m<sub>ch</sub> for tracks with p<sub>T</sub> >100 MeV (p<sub>T</sub> >500 MeV).
The fit parameter μ describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter μ on m<sub>ch</sub> for tracks with p<sub>T</sub> >100 MeV (p<sub>T</sub> >500 MeV).
The fit parameter ν describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter ν on m<sub>ch</sub> for tracks with p<sub>T</sub> >100 MeV (p<sub>T</sub> >500 MeV).
The fit parameter ν describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter ν on m<sub>ch</sub> for tracks with p<sub>T</sub> >100 MeV (p<sub>T</sub> >500 MeV).
The fit parameter ν describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter ν on m<sub>ch</sub> for tracks with p<sub>T</sub> >100 MeV (p<sub>T</sub> >500 MeV).
The fit parameter ν describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter ν on m<sub>ch</sub> for tracks with p<sub>T</sub> >100 MeV (p<sub>T</sub> >500 MeV).
The parameter ξ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m<sub>ch</sub>, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the saturated value of the parameter ξ for m<sub>ch</sub> > 3.0 for tracks with p<sub>T</sub> >100 MeV and for m<sub>ch</sub> > 2.8 for tracks with p<sub>T</sub> >500 MeV, respectively.
The parameter ξ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m<sub>ch</sub>, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the saturated value of the parameter ξ for m<sub>ch</sub> > 3.0 for tracks with p<sub>T</sub> >100 MeV and for m<sub>ch</sub> > 2.8 for tracks with p<sub>T</sub> >500 MeV, respectively.
The parameter ξ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m<sub>ch</sub>, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the saturated value of the parameter ξ for m<sub>ch</sub> > 3.0 for tracks with p<sub>T</sub> >100 MeV and for m<sub>ch</sub> > 2.8 for tracks with p<sub>T</sub> >500 MeV, respectively.
The parameter ξ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m<sub>ch</sub>, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the saturated value of the parameter ξ for m<sub>ch</sub> > 3.0 for tracks with p<sub>T</sub> >100 MeV and for m<sub>ch</sub> > 2.8 for tracks with p<sub>T</sub> >500 MeV, respectively.
The parameter κ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m<sub>ch</sub>, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the exponential fit to the parameter κ for tracks with p<sub>T</sub> >100 MeV and for tracks with p<sub>T</sub> >500 MeV, respectively.
The parameter κ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m<sub>ch</sub>, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the exponential fit to the parameter κ for tracks with p<sub>T</sub> >100 MeV and for tracks with p<sub>T</sub> >500 MeV, respectively.
The parameter κ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m<sub>ch</sub>, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the exponential fit to the parameter κ for tracks with p<sub>T</sub> >100 MeV and for tracks with p<sub>T</sub> >500 MeV, respectively.
The parameter κ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m<sub>ch</sub>, for track p<sub>T</sub>>100 MeV and track p<sub>T</sub>>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the exponential fit to the parameter κ for tracks with p<sub>T</sub> >100 MeV and for tracks with p<sub>T</sub> >500 MeV, respectively.
The two-dimensional dependence on m<sub>ch</sub> and k<sub>T</sub> for p<sub>T</sub> > 500 MeV for the correlation strength, λ, obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions using the MB sample for m<sub>ch</sub> ≤ 3.08 and the HMT sample for m<sub>ch</sub> > 3.08.
The two-dimensional dependence on m<sub>ch</sub> and k<sub>T</sub> for p<sub>T</sub> > 500 MeV for the source radius, R, obtained from the exponential fit to the R<sub>2</sub>(Q) correlation functions using the MB sample for m<sub>ch</sub> ≤ 3.08 and the HMT sample for m<sub>ch</sub> > 3.08.
The parameter λ for p<sub>T</sub> > 500 MeV as a function of k<sub>T</sub> in selected low m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 500 MeV as a function of k<sub>T</sub> in selected low m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 500 MeV as a function of k<sub>T</sub> in selected high m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 500 MeV as a function of k<sub>T</sub> in selected high m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 500 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter λ for p<sub>T</sub> > 500 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 500 MeV as a function of k<sub>T</sub> in selected low m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 500 MeV as a function of k<sub>T</sub> in selected low m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 500 MeV as a function of k<sub>T</sub> in selected high m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 500 MeV as a function of k<sub>T</sub> in selected high m<sub>ch</sub> intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 500 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
The parameter R for p<sub>T</sub> > 500 MeV as a function of m<sub>ch</sub> in k<sub>T</sub> intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.
ATLAS and CMS results for the source radius R as a function of n<sub>ch</sub> in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p<sub>T</sub> > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n<sub>ch</sub> at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.
ATLAS and CMS results for the source radius R as a function of n<sub>ch</sub> in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p<sub>T</sub> > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n<sub>ch</sub> at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.
ATLAS and CMS results for the source radius R as a function of n<sub>ch</sub> in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p<sub>T</sub> > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n<sub>ch</sub> at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.
ATLAS and CMS results for the source radius R as a function of ∛n<sub>ch</sub> in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p<sub>T</sub> > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n<sub>ch</sub> at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.
ATLAS and CMS results for the source radius R as a function of ∛n<sub>ch</sub> in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p<sub>T</sub> > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n<sub>ch</sub> at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.
ATLAS and CMS results for the source radius R as a function of ∛n<sub>ch</sub> in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p<sub>T</sub> > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n<sub>ch</sub> at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.
Systematic uncertainties (in percent) in the correlation strength, λ, and source radius, R, for the exponential fit of the two-particle double-ratio correlation functions, R<sub>2</sub>(Q), for p<sub>T</sub> > 100 MeV at √s= 13 TeV for the MB and HMT events. The choice of MC generator gives rise to asymmetric uncertainties, denoted by uparrow and downarrow. This asymmetry propagates through to the cumulative uncertainty. The columns under ‘Uncertainty range’ show the range of systematic uncertainty from the fits in the various n<sub>ch</sub> intervals.
The results of the fits to the dependencies of the correlation strength, λ, and source radius, R, on the average rescaled charged-particle multiplicity, m<sub>ch</sub>, for |η| < 2.5 and both p<sub>T</sub> > 100 MeV and p<sub>T</sub> > 500 MeV at √s = 13 TeV for the minimum-bias (MB) and the high-multiplicity track (HMT) events. The parameters γ and δ resulting from a joint fit to the MB and HMT data are presented. The total uncertainties are shown.
The results of the fits to the dependencies of the correlation strength, λ, and source radius, R, on the pair average transverse momentum, k<sub>T</sub>, for various functional forms and for minimum-bias (MB) and high-multiplicity track (HMT) events for p<sub>T</sub> > 100 MeV and p<sub>T</sub> > 500 MeV at √s = 13 TeV. The total uncertainties are shown.
The Bose-Einstein correlation (BEC) parameters λ and R as a function of n<sub>ch</sub> and k<sub>T</sub> using different MC generators in the calculation of R<sub>2</sub>(Q). (a) λ versus n<sub>ch</sub> for MB events, (b) λ versus n<sub>ch</sub> for HMT events, (c) λ versus k<sub>T</sub> and (d) R versus k<sub>T</sub> for MB events. The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.
The Bose-Einstein correlation (BEC) parameters λ and R as a function of n<sub>ch</sub> and k<sub>T</sub> using different MC generators in the calculation of R<sub>2</sub>(Q). (a) λ versus n<sub>ch</sub> for MB events, (b) λ versus n<sub>ch</sub> for HMT events, (c) λ versus k<sub>T</sub> and (d) R versus k<sub>T</sub> for MB events. The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.
The Bose-Einstein correlation (BEC) parameters λ and R as a function of n<sub>ch</sub> and k<sub>T</sub> using different MC generators in the calculation of R<sub>2</sub>(Q). (a) λ versus n<sub>ch</sub> for MB events, (b) λ versus n<sub>ch</sub> for HMT events, (c) λ versus k<sub>T</sub> and (d) R versus k<sub>T</sub> for MB events. The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.
The Bose-Einstein correlation (BEC) parameters λ and R as a function of n<sub>ch</sub> and k<sub>T</sub> using different MC generators in the calculation of R<sub>2</sub>(Q). (a) λ versus n<sub>ch</sub> for MB events, (b) λ versus n<sub>ch</sub> for HMT events, (c) λ versus k<sub>T</sub> and (d) R versus k<sub>T</sub> for MB events. The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 10, (b) 11 < n<sub>ch</sub> ≤ 20, (c) 21 < n<sub>ch</sub> ≤ 30, (d) 31 < n<sub>ch</sub> ≤ 40, (e) 41 < n<sub>ch</sub> ≤ 50, (f) 51 < n<sub>ch</sub> ≤ 60, (g) 61 < n<sub>ch</sub> ≤ 70, (h) 71 < n<sub>ch</sub> ≤ 80 and (i) 81 < n<sub>ch</sub> ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 10, (b) 11 < n<sub>ch</sub> ≤ 20, (c) 21 < n<sub>ch</sub> ≤ 30, (d) 31 < n<sub>ch</sub> ≤ 40, (e) 41 < n<sub>ch</sub> ≤ 50, (f) 51 < n<sub>ch</sub> ≤ 60, (g) 61 < n<sub>ch</sub> ≤ 70, (h) 71 < n<sub>ch</sub> ≤ 80 and (i) 81 < n<sub>ch</sub> ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 10, (b) 11 < n<sub>ch</sub> ≤ 20, (c) 21 < n<sub>ch</sub> ≤ 30, (d) 31 < n<sub>ch</sub> ≤ 40, (e) 41 < n<sub>ch</sub> ≤ 50, (f) 51 < n<sub>ch</sub> ≤ 60, (g) 61 < n<sub>ch</sub> ≤ 70, (h) 71 < n<sub>ch</sub> ≤ 80 and (i) 81 < n<sub>ch</sub> ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 10, (b) 11 < n<sub>ch</sub> ≤ 20, (c) 21 < n<sub>ch</sub> ≤ 30, (d) 31 < n<sub>ch</sub> ≤ 40, (e) 41 < n<sub>ch</sub> ≤ 50, (f) 51 < n<sub>ch</sub> ≤ 60, (g) 61 < n<sub>ch</sub> ≤ 70, (h) 71 < n<sub>ch</sub> ≤ 80 and (i) 81 < n<sub>ch</sub> ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 10, (b) 11 < n<sub>ch</sub> ≤ 20, (c) 21 < n<sub>ch</sub> ≤ 30, (d) 31 < n<sub>ch</sub> ≤ 40, (e) 41 < n<sub>ch</sub> ≤ 50, (f) 51 < n<sub>ch</sub> ≤ 60, (g) 61 < n<sub>ch</sub> ≤ 70, (h) 71 < n<sub>ch</sub> ≤ 80 and (i) 81 < n<sub>ch</sub> ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 10, (b) 11 < n<sub>ch</sub> ≤ 20, (c) 21 < n<sub>ch</sub> ≤ 30, (d) 31 < n<sub>ch</sub> ≤ 40, (e) 41 < n<sub>ch</sub> ≤ 50, (f) 51 < n<sub>ch</sub> ≤ 60, (g) 61 < n<sub>ch</sub> ≤ 70, (h) 71 < n<sub>ch</sub> ≤ 80 and (i) 81 < n<sub>ch</sub> ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 10, (b) 11 < n<sub>ch</sub> ≤ 20, (c) 21 < n<sub>ch</sub> ≤ 30, (d) 31 < n<sub>ch</sub> ≤ 40, (e) 41 < n<sub>ch</sub> ≤ 50, (f) 51 < n<sub>ch</sub> ≤ 60, (g) 61 < n<sub>ch</sub> ≤ 70, (h) 71 < n<sub>ch</sub> ≤ 80 and (i) 81 < n<sub>ch</sub> ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 10, (b) 11 < n<sub>ch</sub> ≤ 20, (c) 21 < n<sub>ch</sub> ≤ 30, (d) 31 < n<sub>ch</sub> ≤ 40, (e) 41 < n<sub>ch</sub> ≤ 50, (f) 51 < n<sub>ch</sub> ≤ 60, (g) 61 < n<sub>ch</sub> ≤ 70, (h) 71 < n<sub>ch</sub> ≤ 80 and (i) 81 < n<sub>ch</sub> ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 10, (b) 11 < n<sub>ch</sub> ≤ 20, (c) 21 < n<sub>ch</sub> ≤ 30, (d) 31 < n<sub>ch</sub> ≤ 40, (e) 41 < n<sub>ch</sub> ≤ 50, (f) 51 < n<sub>ch</sub> ≤ 60, (g) 61 < n<sub>ch</sub> ≤ 70, (h) 71 < n<sub>ch</sub> ≤ 80 and (i) 81 < n<sub>ch</sub> ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 91 < n<sub>ch</sub> ≤ 100, (b) 101 < n<sub>ch</sub> ≤ 125, (c) 126 < n<sub>ch</sub> ≤ 150, (d) 151 < n<sub>ch</sub> ≤ 200, (e) 201 < n<sub>ch</sub> ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 91 < n<sub>ch</sub> ≤ 100, (b) 101 < n<sub>ch</sub> ≤ 125, (c) 126 < n<sub>ch</sub> ≤ 150, (d) 151 < n<sub>ch</sub> ≤ 200, (e) 201 < n<sub>ch</sub> ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 91 < n<sub>ch</sub> ≤ 100, (b) 101 < n<sub>ch</sub> ≤ 125, (c) 126 < n<sub>ch</sub> ≤ 150, (d) 151 < n<sub>ch</sub> ≤ 200, (e) 201 < n<sub>ch</sub> ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 91 < n<sub>ch</sub> ≤ 100, (b) 101 < n<sub>ch</sub> ≤ 125, (c) 126 < n<sub>ch</sub> ≤ 150, (d) 151 < n<sub>ch</sub> ≤ 200, (e) 201 < n<sub>ch</sub> ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 91 < n<sub>ch</sub> ≤ 100, (b) 101 < n<sub>ch</sub> ≤ 125, (c) 126 < n<sub>ch</sub> ≤ 150, (d) 151 < n<sub>ch</sub> ≤ 200, (e) 201 < n<sub>ch</sub> ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 101 < n<sub>ch</sub> ≤ 110, (b) 111 < n<sub>ch</sub> ≤ 120, (c) 121 < n<sub>ch</sub> ≤ 130, (d) 131 < n<sub>ch</sub> ≤ 140, (e) 141 < n<sub>ch</sub> ≤ 155, (f) 156 < n<sub>ch</sub> ≤ 175, (g) 176 < n<sub>ch</sub> ≤ 200, (h) 201 < n<sub>ch</sub> ≤ 230 and (i) 231 < n<sub>ch</sub> ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 101 < n<sub>ch</sub> ≤ 110, (b) 111 < n<sub>ch</sub> ≤ 120, (c) 121 < n<sub>ch</sub> ≤ 130, (d) 131 < n<sub>ch</sub> ≤ 140, (e) 141 < n<sub>ch</sub> ≤ 155, (f) 156 < n<sub>ch</sub> ≤ 175, (g) 176 < n<sub>ch</sub> ≤ 200, (h) 201 < n<sub>ch</sub> ≤ 230 and (i) 231 < n<sub>ch</sub> ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 101 < n<sub>ch</sub> ≤ 110, (b) 111 < n<sub>ch</sub> ≤ 120, (c) 121 < n<sub>ch</sub> ≤ 130, (d) 131 < n<sub>ch</sub> ≤ 140, (e) 141 < n<sub>ch</sub> ≤ 155, (f) 156 < n<sub>ch</sub> ≤ 175, (g) 176 < n<sub>ch</sub> ≤ 200, (h) 201 < n<sub>ch</sub> ≤ 230 and (i) 231 < n<sub>ch</sub> ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 101 < n<sub>ch</sub> ≤ 110, (b) 111 < n<sub>ch</sub> ≤ 120, (c) 121 < n<sub>ch</sub> ≤ 130, (d) 131 < n<sub>ch</sub> ≤ 140, (e) 141 < n<sub>ch</sub> ≤ 155, (f) 156 < n<sub>ch</sub> ≤ 175, (g) 176 < n<sub>ch</sub> ≤ 200, (h) 201 < n<sub>ch</sub> ≤ 230 and (i) 231 < n<sub>ch</sub> ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 101 < n<sub>ch</sub> ≤ 110, (b) 111 < n<sub>ch</sub> ≤ 120, (c) 121 < n<sub>ch</sub> ≤ 130, (d) 131 < n<sub>ch</sub> ≤ 140, (e) 141 < n<sub>ch</sub> ≤ 155, (f) 156 < n<sub>ch</sub> ≤ 175, (g) 176 < n<sub>ch</sub> ≤ 200, (h) 201 < n<sub>ch</sub> ≤ 230 and (i) 231 < n<sub>ch</sub> ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 101 < n<sub>ch</sub> ≤ 110, (b) 111 < n<sub>ch</sub> ≤ 120, (c) 121 < n<sub>ch</sub> ≤ 130, (d) 131 < n<sub>ch</sub> ≤ 140, (e) 141 < n<sub>ch</sub> ≤ 155, (f) 156 < n<sub>ch</sub> ≤ 175, (g) 176 < n<sub>ch</sub> ≤ 200, (h) 201 < n<sub>ch</sub> ≤ 230 and (i) 231 < n<sub>ch</sub> ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 101 < n<sub>ch</sub> ≤ 110, (b) 111 < n<sub>ch</sub> ≤ 120, (c) 121 < n<sub>ch</sub> ≤ 130, (d) 131 < n<sub>ch</sub> ≤ 140, (e) 141 < n<sub>ch</sub> ≤ 155, (f) 156 < n<sub>ch</sub> ≤ 175, (g) 176 < n<sub>ch</sub> ≤ 200, (h) 201 < n<sub>ch</sub> ≤ 230 and (i) 231 < n<sub>ch</sub> ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 101 < n<sub>ch</sub> ≤ 110, (b) 111 < n<sub>ch</sub> ≤ 120, (c) 121 < n<sub>ch</sub> ≤ 130, (d) 131 < n<sub>ch</sub> ≤ 140, (e) 141 < n<sub>ch</sub> ≤ 155, (f) 156 < n<sub>ch</sub> ≤ 175, (g) 176 < n<sub>ch</sub> ≤ 200, (h) 201 < n<sub>ch</sub> ≤ 230 and (i) 231 < n<sub>ch</sub> ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 101 < n<sub>ch</sub> ≤ 110, (b) 111 < n<sub>ch</sub> ≤ 120, (c) 121 < n<sub>ch</sub> ≤ 130, (d) 131 < n<sub>ch</sub> ≤ 140, (e) 141 < n<sub>ch</sub> ≤ 155, (f) 156 < n<sub>ch</sub> ≤ 175, (g) 176 < n<sub>ch</sub> ≤ 200, (h) 201 < n<sub>ch</sub> ≤ 230 and (i) 231 < n<sub>ch</sub> ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n<sub>ch</sub> - intervals∶ (a) 2 < n<sub>ch</sub> ≤ 9, (b) 10 < n<sub>ch</sub> ≤ 18, (c) 19 < n<sub>ch</sub> ≤ 27, (d) 28 < n<sub>ch</sub> ≤ 36, (e) 37 < n<sub>ch</sub> ≤ 45, (f) 46 < n<sub>ch</sub> ≤ 54, (g) 55 < n<sub>ch</sub> ≤ 63, (h) 64 < n<sub>ch</sub> ≤ 72, (i) 73 < n<sub>ch</sub> ≤ 81, (j) 82 < n<sub>ch</sub> ≤ 90, (k) 91 < n<sub>ch</sub> ≤ 113, and (l) 114 < n<sub>ch</sub> ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The single-ratio two-particle correlation functions, C<sub>2</sub><sup>data</sup>(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k<sub>T</sub> - intervals∶ (a) 100 < k<sub>T</sub> ≤ 200 MeV, (b) 200 < k<sub>T</sub> ≤ 300 MeV, (c) 300 < k<sub>T</sub> ≤ 400 MeV, (d) 400 < k<sub>T</sub> ≤ 500 MeV, (e) 500 < k<sub>T</sub> ≤ 600 MeV, (f) 600 < k<sub>T</sub> ≤ 700 MeV, (g) 700 < k<sub>T</sub> ≤ 1000 MeV, and (h) 1000 < k<sub>T</sub> ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.
The correlation strength, λ, and source radius, R, of the exponential fits to the two-particle double-ratio correlation functions, R<sub>2</sub>(Q), in dependence on the multiplicity, m<sub>ch</sub>, intervals for the minimum-bias (MB) and the high-multiplicity track (HMT) events for p<sub>T</sub> > 100 MeV at √s = 13 TeV. Statistical uncertainties for √χ<sup>2</sup>/ndf>1 are corrected by the √χ<sup>2</sup>/ndf. The total uncertainties are shown.
The correlation strength, λ, and source radius, R, of the exponential fits to the two-particle double-ratio correlation functions, R<sub>2</sub>(Q), in dependence on the multiplicity, m<sub>ch</sub>, intervals for the minimum-bias (MB) and the high-multiplicity track (HMT) events for p<sub>T</sub> > 500 MeV at √s = 13 TeV. Statistical uncertainties for √χ<sup>2</sup>/ndf>1 are corrected by the √χ<sup>2</sup>/ndf. The total uncertainties are shown.
The correlation strength, λ, and source radius, R, of the exponential fits to the two-particle double-ratio correlation functions, R<sub>2</sub>(Q), in dependence on the pair transverse momentum, k<sub>T</sub>, intervals for the minimum-bias (MB) and the high-multiplicity track (HMT) events for p<sub>T</sub> > 100 MeV at √s = 13 TeV. Statistical uncertainties for √χ<sup>2</sup>/ndf>1 are corrected by the √χ<sup>2</sup>/ndf. The total uncertainties are shown.
The correlation strength, λ, and source radius, R, of the exponential fits to the two-particle double-ratio correlation functions, R<sub>2</sub>(Q), in dependence on the pair transverse momentum, k<sub>T</sub>, intervals for the minimum-bias (MB) and the high-multiplicity track (HMT) events for p<sub>T</sub> > 500 MeV at √s = 13 TeV. Statistical uncertainties for √χ<sup>2</sup>/ndf>1 are corrected by the √χ<sup>2</sup>/ndf. The total uncertainties are shown.
A search for a chargino$-$neutralino pair decaying via the 125 GeV Higgs boson into photons is presented. The study is based on the data collected between 2015 and 2018 with the ATLAS detector at the LHC, corresponding to an integrated luminosity of 139 fb$^{-1}$ of $pp$ collisions at a centre-of-mass energy of 13 TeV. No significant excess over the expected background is observed. Upper limits at 95% confidence level for a massless $\tilde{\chi}^{0}_{1}$ are set on several electroweakino production cross-sections and the visible cross-section for beyond the Standard Model processes. In the context of simplified supersymmetric models, 95% confidence-level limits of up to 310 GeV in $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$, where $m(\tilde{\chi}^{0}_{1})=0.5$ GeV, are set. Limits at 95% confidence level are also set on the $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ cross-section in the mass plane of $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$ and $m(\tilde{\chi}^{0}_{1})$, and on scenarios with gravitino as the lightest supersymmetric particle. Upper limits at the 95% confidence-level are set on the higgsino production cross-section. Higgsino masses below 380 GeV are excluded for the case of the higgsino fully decaying into a Higgs boson and a gravitino.
The 95% CL model-independent upper limits computed from individual fits in each of 12 categories on the visible cross-section $\sigma_{\mathrm{vis}}^{\mathrm{BSM}} = \sigma \times A \times \epsilon$ for any $pp\to h(125~GeV) + E^{miss}_{T} \to \gamma\gamma + E^{miss}_{T}$ BSM processes.
Expected and observed 95% CL exclusion upper limits on the production cross-section of $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2} \to W^{\pm}\tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1}$ as a function of $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$.
The observed exclusion limit contours at 95% CL for the $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ production in the $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$-$m(\tilde{\chi}^{0}_{1})$ plane.
The expected exclusion limit contours at 95% CL for the $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ production in the $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$-$m(\tilde{\chi}^{0}_{1})$ plane.
The +1 $\sigma$ expected exclusion limit contours at 95% CL for the $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ production in the $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$-$m(\tilde{\chi}^{0}_{1})$ plane.
The -1 $\sigma$ expected exclusion limit contours at 95% CL for the $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ production in the $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$-$m(\tilde{\chi}^{0}_{1})$ plane.
The +1 $\sigma$ observed exclusion limit contours at 95% CL for the $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ production in the $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$-$m(\tilde{\chi}^{0}_{1})$ plane.
The -1 $\sigma$ observed exclusion limit contours at 95% CL for the $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ production in the $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$-$m(\tilde{\chi}^{0}_{1})$ plane.
The observed and expected exclusion limit at 95% CL for the $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ production in the $m(\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2})$-$m(\tilde{\chi}^{0}_{1})$ plane.
Expected and observed 95% CL exclusion upper limits on the higgsino production ($\tilde{\chi}\tilde{\chi} \equiv \tilde{\chi}^{0}_{1}\tilde{\chi}^{0}_{2}, \tilde{\chi}^{0}_{1}\tilde{\chi}^{\pm}_{1}, \tilde{\chi}^{0}_{2}\tilde{\chi}^{\pm}_{1}, \tilde{\chi}^{\pm}_{1}\tilde{\chi}^{\mp}_{1}$) cross-section in the signal of $h \tilde{G} h \tilde{G} $ as a function of the higgsino mass.
The distribution of $S_{E_\mathrm{T}^{\mathrm{miss}}}$ after the selection of diphoton candidates with $120~GeV < m_{\gamma\gamma} < 130~GeV$. Expected distributions are shown for the $\tilde{\chi}^{\pm}_{1} \tilde{\chi}^{0}_{2} \to W^{\pm} \tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1} $ signal with $m(\tilde{\chi}^{\pm}_{1} \tilde{\chi}^{0}_{2})=200~GeV$ and $m(\tilde{\chi}^{0}_{1})=0.5~GeV$, and the $h\tilde{G} h\tilde{G}$ signal with $m(\tilde{\chi}^{0}_{1})=150~GeV$ and $m(\tilde{G})=1~MeV$. These overlaid signal points are representative of the model kinematics. The sum in quadrature of the MC statistical and experimental systematic uncertainties in the total background is shown as the hatched bands, while the theoretical uncertainties in the background normalisation are not included. The $t\bar{t}\gamma$ and $t\bar{t}\gamma\gamma$ processes have a negligible contribution and are not represented. Overflow events are included in the rightmost bin. The lower panel shows the ratio of the data to the background.
Acceptance and efficiency for the $W^{\pm} \tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1}$ signal in each category.
Acceptance and efficiency for the $h \tilde{G} h \tilde{G}$ signal in each category.
Cut flow for the $W^{\pm} \tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1}$ signal sample at $m(\tilde{\chi}^{\pm}_{1} \tilde{\chi}^{0}_{2})=200~GeV$, $m(\tilde{\chi}^{0}_{1})=0.5~GeV$, with 260000 entries.
Cut flow for the $h \tilde{G} h \tilde{G}$ signal sample at $m(\tilde{\chi}^{0}_{1})=150~GeV$, $m(\tilde{G})=1~MeV$, with 50000 entries.
Event yields in the range 120 <$m\gamma\gamma$< 130 GeV for data, signal models, the SM Higgs boson background and non-resonant background in each analysis category after the fit.
Event yields in the range 120 <$m\gamma\gamma$< 130 GeV for data, signal models, the SM Higgs boson background and non-resonant background in each analysis category before the fit.
Acceptance for the $W^{\pm} \tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1}$ signals in the leptonic channel.
Efficiency for the $W^{\pm} \tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1}$ signals in the leptonic channel.
Acceptance for the $W^{\pm} \tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1}$ signals in the hadronic channel.
Efficiency for the $W^{\pm} \tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1}$ signals in the hadronic channel.
Acceptance for the $W^{\pm} \tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1}$ signals in the rest channel.
Efficiency for the $W^{\pm} \tilde{\chi}^{0}_{1} h \tilde{\chi}^{0}_{1}$ signals in the rest channel.
Acceptance for the $h \tilde{G} h \tilde{G}$ signals in the each channel.
Efficiency for the $h \tilde{G} h \tilde{G}$ signals in the each channel.
The prevalence of hadronic jets at the LHC requires that a deep understanding of jet formation and structure is achieved in order to reach the highest levels of experimental and theoretical precision. There have been many measurements of jet substructure at the LHC and previous colliders, but the targeted observables mix physical effects from various origins. Based on a recent proposal to factorize physical effects, this Letter presents a double-differential cross-section measurement of the Lund jet plane using 139 fb$^{-1}$ of $\sqrt{s}=13$ TeV proton-proton collision data collected with the ATLAS detector using jets with transverse momentum above 675 GeV. The measurement uses charged particles to achieve a fine angular resolution and is corrected for acceptance and detector effects. Several parton shower Monte Carlo models are compared with the data. No single model is found to be in agreement with the measured data across the entire plane.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for use in MC tuning.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 0.00 < ln(R/#DeltaR) < 0.33.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 0.33 < ln(R/#DeltaR) < 0.67.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 0.67 < ln(R/#DeltaR) < 1.00.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 1.00 < ln(R/#DeltaR) < 1.33.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 1.33 < ln(R/#DeltaR) < 1.67.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 1.67 < ln(R/#DeltaR) < 2.00.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 2.00 < ln(R/#DeltaR) < 2.33.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 2.33 < ln(R/#DeltaR) < 2.67.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 2.67 < ln(R/#DeltaR) < 3.00.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 3.00 < ln(R/#DeltaR) < 3.33.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 3.33 < ln(R/#DeltaR) < 3.67.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 3.67 < ln(R/#DeltaR) < 4.00.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single vertical slice of the Lund jet plane between 4.00 < ln(R/#DeltaR) < 4.33.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 0.69 < ln(1/z) < 0.97.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 0.97 < ln(1/z) < 1.25.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 1.25 < ln(1/z) < 1.52.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 1.52 < ln(1/z) < 1.80.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 1.80 < ln(1/z) < 2.08.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 2.08 < ln(1/z) < 2.36.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 2.36 < ln(1/z) < 2.63.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 2.63 < ln(1/z) < 2.91.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 2.91 < ln(1/z) < 3.19.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 3.19 < ln(1/z) < 3.47.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 3.47 < ln(1/z) < 3.74.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 3.74 < ln(1/z) < 4.02.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 4.02 < ln(1/z) < 4.30.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 4.30 < ln(1/z) < 4.57.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 4.57 < ln(1/z) < 4.85.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 4.85 < ln(1/z) < 5.13.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 5.13 < ln(1/z) < 5.41.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 5.41 < ln(1/z) < 5.68.
Normalized differential cross-section of the Lund jet plane. The first systematic uncertainty is detector systematics, the second is background systematic uncertainties. The data is presented as a 1D distribution, for a single horizontal slice of the Lund jet plane between 5.68 < ln(1/z) < 5.96.
The summed covariance matrix of all systematic and statistical uncertainties associated with the measurement in bins of $\ln{(1/z)} \times \ln{(R/\Delta R)}$.
The summed covariance matrix of all statistical uncertainties associated with the measurement in bins of $\ln{(1/z)} \times \ln{(R/\Delta R)}$.
A search for long-lived particles decaying into hadrons and at least one muon is presented. The analysis selects events that pass a muon or missing-transverse-momentum trigger and contain a displaced muon track and a displaced vertex. The analyzed dataset of proton-proton collisions at $\sqrt{s} = 13$ TeV was collected with the ATLAS detector and corresponds to 136 fb$^{-1}$. The search employs dedicated reconstruction techniques that significantly increase the sensitivity to long-lived particle decays that occur in the ATLAS inner detector. Background estimates for Standard Model processes and instrumental effects are extracted from data. The observed event yields are compatible with those expected from background processes. The results are presented as limits at 95% confidence level on model-independent cross sections for processes beyond the Standard Model, and interpreted as exclusion limits in scenarios with pair-production of long-lived top squarks that decay via a small $R$-parity-violating coupling into a quark and a muon. Top squarks with masses up to 1.7 TeV are excluded for a lifetime of 0.1 ns, and masses below 1.3 TeV are excluded for lifetimes between 0.01 ns and 30 ns.
Vertex selection acceptance for the $\tilde{t}$ $R$-hadron benchmark model as a function of the transverse decay distance $r_{DV}$.
Vertex selection acceptance for the $\tilde{t}$ $R$-hadron benchmark model as a function of the transverse decay distance $r_{DV}$.
Vertex selection efficiency for the $\tilde{t}$ $R$-hadron benchmark model as a function of the transverse decay distance $r_{DV}$.
Vertex selection efficiency for the $\tilde{t}$ $R$-hadron benchmark model as a function of the transverse decay distance $r_{DV}$.
Track multiplicity $n_{Tracks}$ for preselected DVs in MET-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.
Track multiplicity $n_{Tracks}$ for preselected DVs in MET-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.
Track multiplicity $n_{Tracks}$ for preselected DVs in muon-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.
Track multiplicity $n_{Tracks}$ for preselected DVs in muon-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.
Invariant mass $m_{DV}$ for the highest-mass preselected DV with at least three associated tracks in MET-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.
Invariant mass $m_{DV}$ for the highest-mass preselected DV with at least three associated tracks in MET-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.
Invariant mass $m_{DV}$ for the highest-mass preselected DV with at least three associated tracks in muon-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.
Invariant mass $m_{DV}$ for the highest-mass preselected DV with at least three associated tracks in muon-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.
The observed event yields in the control, validation and signal regions are shown for the MET Trigger selections, along with the predicted background yields. The bottom panel shows the ratio of observed events to the total background yields. The errors represent the total uncertainty of the backgrounds prediction, including the statistical and systematic uncertainties added in quadrature.
The observed event yields in the control, validation and signal regions are shown for the MET Trigger selections, along with the predicted background yields. The bottom panel shows the ratio of observed events to the total background yields. The errors represent the total uncertainty of the backgrounds prediction, including the statistical and systematic uncertainties added in quadrature.
The observed event yields in the control, validation and signal regions are shown for the Muon Trigger selections, along with the predicted background yields. The bottom panel shows the ratio of observed events to the total background yields. The errors represent the total uncertainty of the backgrounds prediction, including the statistical and systematic uncertainties added in quadrature.
The observed event yields in the control, validation and signal regions are shown for the Muon Trigger selections, along with the predicted background yields. The bottom panel shows the ratio of observed events to the total background yields. The errors represent the total uncertainty of the backgrounds prediction, including the statistical and systematic uncertainties added in quadrature.
Expected exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Expected exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Expected (1 sigma band) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Expected (1 sigma band) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Expected (2 sigma band) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Expected (2 sigma band) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Observed exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Observed exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Observed (+1 sigma) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Observed (+1 sigma) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Observed (-1 sigma) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Observed (-1 sigma) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.
Exclusion limits on the production cross section as a function of m($\tilde{t}$) are shown for several values of $\tau(\tilde{t})$ along with the nominal signal production cross section and its theoretical uncertainty.
Exclusion limits on the production cross section as a function of m($\tilde{t}$) are shown for several values of $\tau(\tilde{t})$ along with the nominal signal production cross section and its theoretical uncertainty.
Parameterized event selection efficiencies for the $E_{T}^{miss}$ Trigger SR. The event-level efficiencies for each SR are extracted for all events passing the acceptance of the corresponding SR.
Parameterized event selection efficiencies for the $E_{T}^{miss}$ Trigger SR. The event-level efficiencies for each SR are extracted for all events passing the acceptance of the corresponding SR.
Parameterized event selection efficiencies for the Muon Trigger SR. The event-level efficiencies for each SR are extracted for all events passing the acceptance of the corresponding SR.
Parameterized event selection efficiencies for the Muon Trigger SR. The event-level efficiencies for each SR are extracted for all events passing the acceptance of the corresponding SR.
Parameterized muon-level reconstruction efficiencies as a function of the muon $p_{T}$ and $d_{0}$. The muon-level efficiencies are extracted using muons passing the muon acceptance criteria.
Parameterized muon-level reconstruction efficiencies as a function of the muon $p_{T}$ and $d_{0}$. The muon-level efficiencies are extracted using muons passing the muon acceptance criteria.
Parameterized vertex-level reconstruction efficiencies as a function of the radial position of the truth vertex. The efficiency is calculated independent of the muons originating from this truth vertex.
Parameterized vertex-level reconstruction efficiencies as a function of the radial position of the truth vertex. The efficiency is calculated independent of the muons originating from this truth vertex.
Parameterized vertex-level reconstruction efficiencies as a function of the radial position of the truth vertex. The efficiency is calculated only for truth vertices which have a muon originating from them which is matched to a reconstructed muon.
Parameterized vertex-level reconstruction efficiencies as a function of the radial position of the truth vertex. The efficiency is calculated only for truth vertices which have a muon originating from them which is matched to a reconstructed muon.
The $p_{T}$ distribution of all muons originating from LLP decays in the samples used to calculate and validate the efficiencies.
The $p_{T}$ distribution of all muons originating from LLP decays in the samples used to calculate and validate the efficiencies.
The invariant mass and multiplicity of selected decay products of all truth vertices used in the calculation and validation of the reconstructed efficiencies.
The invariant mass and multiplicity of selected decay products of all truth vertices used in the calculation and validation of the reconstructed efficiencies.
A measurement of the $B^0_s \to J/\psi\phi$ decay parameters using 80.5 $\mathrm{fb}^{-1}$ of integrated luminosity collected with the ATLAS detector from 13 TeV proton-proton collisions at the LHC is presented. The measured parameters include the $CP$-violating phase $\phi_s$, the width difference $\Delta\Gamma_{s}$ between the $B^0_s$ meson mass eigenstates and the average decay width $\Gamma_{s}$. The values measured for the physical parameters are combined with those from 19.2 $\mathrm{fb}^{-1}$ of 7 TeV and 8 TeV data, leading to the following: \begin{eqnarray*} \phi_s & = & -0.087\phantom{0} \pm 0.036\phantom{0} ~\mathrm{(stat.)} \pm 0.021\phantom{0} ~\mathrm{(syst.)~rad} \\ \Delta\Gamma_{s} & = & \phantom{-}0.0657 \pm 0.0043 ~\mathrm{(stat.)} \pm 0.0037 ~\mathrm{(syst.)~ps}^{-1} \\ \Gamma_{s} & = & \phantom{-}0.6703 \pm 0.0014 ~\mathrm{(stat.)} \pm 0.0018 ~\mathrm{(syst.)~ps}^{-1} \\ \end{eqnarray*} Results for $\phi_s$ and $\Delta\Gamma_{s}$ are also presented as 68% confidence level contours in the $\phi_s$-$\Delta\Gamma_{s}$ plane. Furthermore, the transversity amplitudes and corresponding strong phases are measured. $\phi_s$ and $\Delta\Gamma_{s}$ measurements are in agreement with the Standard Model predictions.
Fitted values for the physical parameters of interest with their statistical and systematic uncertainties, for the result of solution (a).
Fitted values for the physical parameters of interest with their statistical and systematic uncertainties, for the result of solution (b).
Fit correlations between the physical parameters of interest, obtained from the fit for solution (a).
Fit correlations between the physical parameters of interest, obtained from the fit for solution (b).
Values of the physical parameters extracted in the combination of solution (a) of 13 TeV results with those obtained from 7 TeV and 8 TeV data.
Values of the physical parameters extracted in the combination of solution (b) of 13 TeV results with those obtained from 7 TeV and 8 TeV data.
Correlation matrix of the BLUE combination of the 7 TeV and 8 TeV results and the solution (a) of the 13 TeV result.
Correlation matrix of the BLUE combination of the 7 TeV and 8 TeV results and the solution (b) of the 13 TeV result.
Summary of systematic uncertainties assigned to the physical parameters of interest.
A search for Higgs boson pair production via vector-boson fusion (VBF) in the $b\bar{b}b\bar{b}$ final state is carried out with the ATLAS experiment using 126 fb$^{-1}$ of proton-proton collision data delivered at $\sqrt{s} = 13$ TeV by the Large Hadron Collider. This search is sensitive to VBF production of additional heavy bosons that may decay into Higgs boson pairs, and in a non-resonant topology it can constrain the quartic coupling between the Higgs bosons and vector bosons. No significant excess relative to the Standard Model expectation is observed, and limits on the production cross-section are set at the 95 % confidence level for a heavy scalar resonance in the context of an extended Higgs sector, and for non-resonant Higgs boson pair production. Interpretation in terms of the coupling between a Higgs boson pair and two vector bosons is also provided: coupling values normalised to the Standard Model expectation of $\kappa_{2V} < -0.43$ and $\kappa_{2V} > 2.56$ are excluded at the 95 % confidence level in data.
Acceptance x efficiency versus $\kappa_{2V}$ for non-resonant signal of $HH$.
Acceptance x efficiency versus $\kappa_{2V}$ for non-resonant signal of $HH$.
Acceptance x efficiency versus $\kappa_{2V}$ for non-resonant signal of $HH$.
Acceptance x efficiency versus $\kappa_{2V}$ for non-resonant signal of $HH$.
Acceptance x efficiency versus resonance mass for both narrow and broad resonance $X$ to $HH$.
Acceptance x efficiency versus resonance mass for both narrow and broad resonance $X$ to $HH$.
Acceptance x efficiency versus resonance mass for both narrow and broad resonance $X$ to $HH$.
Acceptance x efficiency versus resonance mass for both narrow and broad resonance $X$ to $HH$.
Post-fit mass distribution of the $HH$ candidates in the signal region. The expected background is shown after the profile-likelihood fit to data with the background-only hypothesis; the narrow-width resonant signal at 800 GeV and the non-resonant signal at $\kappa_{2V}$ = 3 are overlaid, both normalised to the corresponding observed upper limits on the cross-section.
Post-fit mass distribution of the $HH$ candidates in the signal region. The expected background is shown after the profile-likelihood fit to data with the background-only hypothesis; the narrow-width resonant signal at 800 GeV and the non-resonant signal at $\kappa_{2V}$ = 3 are overlaid, both normalised to the corresponding observed upper limits on the cross-section.
Post-fit mass distribution of the $HH$ candidates in the signal region. The expected background is shown after the profile-likelihood fit to data with the background-only hypothesis; the narrow-width resonant signal at 800 GeV and the non-resonant signal at $\kappa_{2V}$ = 3 are overlaid, both normalised to the corresponding observed upper limits on the cross-section.
Post-fit mass distribution of the $HH$ candidates in the signal region. The expected background is shown after the profile-likelihood fit to data with the background-only hypothesis; the narrow-width resonant signal at 800 GeV and the non-resonant signal at $\kappa_{2V}$ = 3 are overlaid, both normalised to the corresponding observed upper limits on the cross-section.
Observed and expected limits at 95% CL on the cross-sections of narrow resonance $X$ to $HH$ as a function of the mass $m_{X}$.
Observed and expected limits at 95% CL on the cross-sections of narrow resonance $X$ to $HH$ as a function of the mass $m_{X}$.
Observed and expected limits at 95% CL on the cross-sections of narrow resonance $X$ to $HH$ as a function of the mass $m_{X}$.
Observed and expected limits at 95% CL on the cross-section of narrow resonance $X$ to $HH$ as a function of the mass $m_{X}$.
Observed and expected limits at 95% CL on the cross-sections of broad resonance $X$ to $HH$ as a function of the mass $m_{X}$.
Observed and expected limits at 95% CL on the cross-sections of broad resonance $X$ to $HH$ as a function of the mass $m_{X}$.
Observed and expected limits at 95% CL on the cross-sections of broad resonance $X$ to $HH$ as a function of the mass $m_{X}$.
Observed and expected limits at 95% CL on the cross-section of broad resonance $X$ to $HH$ as a function of the mass $m_{X}$.
Observed and expected limits at 95% CL on the cross-sections of non-resonant $HH$ production via VBF as a function of the di-vector-boson-di-Higgs-boson coupling modifier $\kappa_{2V}$. The theory prediction of the cross-section as a function of $\kappa_{2V}$ is also shown. The minimum theory cross-section is 1.37 fb at $\kappa_{2V}=1.17$.
Observed and expected limits at 95% CL on the cross-sections of non-resonant $HH$ production via VBF as a function of the di-vector-boson-di-Higgs-boson coupling modifier $\kappa_{2V}$. The theory prediction of the cross-section as a function of $\kappa_{2V}$ is also shown. The minimum theory cross-section is 1.37 fb at $\kappa_{2V}=1.17$.
Observed and expected limits at 95% CL on the cross-sections of non-resonant $HH$ production via VBF as a function of the di-vector-boson-di-Higgs-boson coupling modifier $\kappa_{2V}$. The theory prediction of the cross-section as a function of $\kappa_{2V}$ is also shown. The minimum theory cross-section is 1.37 fb at $\kappa_{2V}=1.17$.
Observed and expected limits at 95% CL on the cross-sections of non-resonant $HH$ production via VBF as a function of the di-vector-boson-di-Higgs-boson coupling modifier $\kappa_{2V}$. The theory prediction of the cross-section as a function of $\kappa_{2V}$ is also shown. The minimum theory cross-section is 1.37 fb at $\kappa_{2V}=1.17$.
The dynamics of isolated-photon plus two-jet production in $pp$ collisions at a centre-of-mass energy of 13 TeV are studied with the ATLAS detector at the LHC using a dataset corresponding to an integrated luminosity of 36.1 fb$^{-1}$. Cross sections are measured as functions of a variety of observables, including angular correlations and invariant masses of the objects in the final state, $\gamma+jet+jet$. Measurements are also performed in phase-space regions enriched in each of the two underlying physical mechanisms, namely direct and fragmentation processes. The measurements cover the range of photon (jet) transverse momenta from 150 GeV (100 GeV) to 2 TeV. The tree-level plus parton-shower predictions from SHERPA and PYTHIA as well as the next-to-leading-order QCD predictions from SHERPA are compared with the measurements. The next-to-leading-order QCD predictions describe the data adequately in shape and normalisation except for regions of phase space such as those with high values of the invariant mass or rapidity separation of the two jets, where the predictions overestimate the data.
Measured cross sections for isolated-photon plus two-jet production as functions of $E_{\mathrm{T}}^{\gamma}$ for the total phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $p_{\mathrm{T}}^{\textrm{jet}}$ for the total phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $|y^{\textrm{jet}}|$ for the total phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta y^{\gamma-\textrm{jet}}$ for the total phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta \phi^{\gamma-\textrm{jet}}$ for the total phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta y^{\textrm{jet}-\textrm{jet}}$ for the total phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta \phi^{\textrm{jet}-\textrm{jet}}$ for the total phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $m^{\textrm{jet}-\textrm{jet}}$ for the total phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $m^{\gamma-\textrm{jet}-\textrm{jet}}$ for the total phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $E_{\mathrm{T}}^{\gamma}$ for the fragmentation-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $p_{\mathrm{T}}^{\textrm{jet}}$ for the fragmentation-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $|y^{\textrm{jet}}|$ for the fragmentation-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta y^{\gamma-\textrm{jet}}$ for the fragmentation-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta \phi^{\gamma-\textrm{jet}}$ for the fragmentation-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta y^{\textrm{jet}-\textrm{jet}}$ for the fragmentation-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta \phi^{\textrm{jet}-\textrm{jet}}$ for the fragmentation-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $m^{\textrm{jet}-\textrm{jet}}$ for the fragmentation-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $m^{\gamma-\textrm{jet}-\textrm{jet}}$ for the fragmentation-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $E_{\mathrm{T}}^{\gamma}$ for the direct-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $p_{\mathrm{T}}^{\textrm{jet}}$ for the direct-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $|y^{\textrm{jet}}|$ for the direct-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta y^{\gamma-\textrm{jet}}$ for the direct-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta \phi^{\gamma-\textrm{jet}}$ for the direct-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta y^{\textrm{jet}-\textrm{jet}}$ for the direct-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $\Delta \phi^{\textrm{jet}-\textrm{jet}}$ for the direct-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $m^{\textrm{jet}-\textrm{jet}}$ for the direct-enriched phase-space. The predictions from Sherpa NLO are also included.
Measured cross sections for isolated-photon plus two-jet production as functions of $m^{\gamma-\textrm{jet}-\textrm{jet}}$ for the direct-enriched phase-space. The predictions from Sherpa NLO are also included.
Jet substructure quantities are measured using jets groomed with the soft-drop grooming procedure in dijet events from 32.9 fb$^{-1}$ of $pp$ collisions collected with the ATLAS detector at $\sqrt{s} = 13$ TeV. These observables are sensitive to a wide range of QCD phenomena. Some observables, such as the jet mass and opening angle between the two subjets which pass the soft-drop condition, can be described by a high-order (resummed) series in the strong coupling constant $\alpha_S$. Other observables, such as the momentum sharing between the two subjets, are nearly independent of $\alpha_S$. These observables can be constructed using all interacting particles or using only charged particles reconstructed in the inner tracking detectors. Track-based versions of these observables are not collinear safe, but are measured more precisely, and universal non-perturbative functions can absorb the collinear singularities. The unfolded data are directly compared with QCD calculations and hadron-level Monte Carlo simulations. The measurements are performed in different pseudorapidity regions, which are then used to extract quark and gluon jet shapes using the predicted quark and gluon fractions in each region. All of the parton shower and analytical calculations provide an excellent description of the data in most regions of phase space.
Data from Fig 6a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6c. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6c. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6d. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6d. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6e. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6e. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6f. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 6f. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 7a. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7a. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7b. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7b. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7c. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7c. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7d. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7d. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7e. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7e. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7f. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 7f. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 8a. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8a. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8b. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8b. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8c. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8c. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8d. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8d. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8e. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8e. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8f. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 8f. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 4b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 4b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 21b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 21b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5a. The unfolded $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5a. The unfolded $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14d. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14d. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4d. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4d. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5d. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5d. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14e. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14e. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14f. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14f. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4e. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4e. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4f. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4f. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5e. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5e. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5f. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5f. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 4a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 4a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 4b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 4b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5a. The unfolded $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5a. The unfolded $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 5b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 14c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14d. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14d. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4d. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4d. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5d. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5d. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14e. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14e. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14f. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 14f. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4e. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4e. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4f. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 4f. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5e. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5e. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5f. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 5f. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 36-40a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 36-40a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in (300, 400, 600, 800, 1000, infinity) and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 81-85a. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 81-85a. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 36-40b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 36-40b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 81-85b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 81-85b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 36-40c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 36-40c. The unfolded $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 81-85c. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 81-85c. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 51-55a. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 51-55a. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 101-105a. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 101-105a. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 51-55b. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 51-55b. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 101-105b. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 101-105b. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 51-55c. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 51-55c. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 101-105c. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 101-105c. The unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 66-70a. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 66-70a. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110a. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110a. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 66-70b. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 66-70b. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110b. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110b. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 66-70c. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 66-70c. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110c. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110c. The unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 26-30a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 26-30a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 71-75a. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 71-75a. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 26-30b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 26-30b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 71-75b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 71-75b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 26-30c. The unfolded $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 26-30c. The unfolded $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 71-75c. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 71-75c. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 41-45a. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 41-45a. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 86-90a. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 86-90a. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 41-45b. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 41-45b. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 86-90b. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 86-90b. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 41-45c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 41-45c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 86-90c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 86-90c. The unfolded all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 56-60a. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 56-60a. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 101-105a. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 101-105a. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 56-60b. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 56-60b. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 101-105b. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 101-105b. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 56-60c. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 56-60c. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 101-105c. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 101-105c. The unfolded all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 31-35a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 31-35a. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 76-80a. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 76-80a. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 31-35b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 31-35b. The unfolded all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 76-80b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 76-80b. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 31-35c. The unfolded $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 31-35c. The unfolded $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 76-80c. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 76-80c. The unfolded charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from Fig 46-50a. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 46-50a. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 91-95a. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 91-95a. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 46-50b. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 46-50b. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 91-95b. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 91-95b. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 46-50c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 46-50c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 91-95c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 91-95c. The unfolded all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from Fig 61-65a. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 61-65a. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110a. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110a. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 61-65b. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 61-65b. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110b. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110b. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 61-65c. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 61-65c. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110c. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from Fig 106-110c. The unfolded all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 6a. The extracted quark-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6a. The extracted quark-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15a. Theextracted quark-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15a. Theextracted quark-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6b. The extracted quark-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6b. The extracted quark-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15b. The extracted quark-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15b. The extracted quark-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6c. The extracted quark-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6c. The extracted quark-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15c. The extracted quark-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15c. The extracted quark-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 7a. The extracted quark-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7a. The extracted quark-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16a. The extracted quark-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16a. The extracted quark-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7b. The extracted quark-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7b. The extracted quark-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16b. The extracted quark-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16b. The extracted quark-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7c. The extracted quark-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7c. The extracted quark-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16c. The extracted quark-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16c. The extracted quark-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8a. The extracted quark-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8a. The extracted quark-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17a. The extracted quark-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17a. The extracted quark-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8b. The extracted quark-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8b. The extracted quark-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17b. The extracted quark-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17b. The extracted quark-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8c. The extracted quark-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8c. The extracted quark-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17c. The extracted quark-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17c. The extracted quark-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 6a. The extracted gluon-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6a. The extracted gluon-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15a. Theextracted gluon-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15a. Theextracted gluon-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6b. The extracted gluon-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6b. The extracted gluon-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15b. The extracted gluon-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15b. The extracted gluon-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6c. The extracted gluon-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 6c. The extracted gluon-distribution from the unfolded all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15c. The extracted gluon-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from Fig 15c. The extracted gluon-distribution from the unfolded charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 7a. The extracted gluon-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7a. The extracted gluon-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16a. The extracted gluon-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16a. The extracted gluon-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7b. The extracted gluon-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7b. The extracted gluon-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16b. The extracted gluon-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16b. The extracted gluon-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7c. The extracted gluon-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 7c. The extracted gluon-distribution from the unfolded all-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16c. The extracted gluon-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 16c. The extracted gluon-distribution from the unfolded charged-particle $z_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8a. The extracted gluon-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8a. The extracted gluon-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17a. The extracted gluon-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17a. The extracted gluon-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8b. The extracted gluon-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8b. The extracted gluon-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17b. The extracted gluon-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17b. The extracted gluon-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8c. The extracted gluon-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 8c. The extracted gluon-distribution from the unfolded all-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17c. The extracted gluon-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from Fig 17c. The extracted gluon-distribution from the unfolded charged-particle $R_g$ distribution for anti-kt R=0.8 jets with 600 < $p_T$ < 800 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. All uncertainties described in the text are shown on the data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 99a. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 99a. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 100a. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 100a. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 99b. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 99b. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 100b. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 100b. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 99c. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 99c. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 100c. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 100c. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 101a. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 101a. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 102a. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 102a. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 101b. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 101b. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 102b. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 102b. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 101c. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 101c. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 102c. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 102c. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 103a. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 103a. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 104a. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 104a. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 103b. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 103b. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 104b. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 104b. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 103c. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 103c. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 104c. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 104c. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 105a. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 105a. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 106a. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 106a. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 105b. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 105b. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 106b. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 106b. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 105c. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 105c. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 106c. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 106c. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 107a. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 107a. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 108a. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 108a. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 107b. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 107b. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 108b. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 108b. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 107c. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 107c. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 108c. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 108c. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 109a. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 109a. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 110a. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 110a. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 109b. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 109b. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 110b. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 110b. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 109c. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 109c. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 110c. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 110c. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 111a. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111a. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112a. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112a. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111b. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111b. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112b. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112b. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111c. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111c. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112c. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112c. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 113a. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 113a. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 114a. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 114a. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 113b. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 113b. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 114b. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 114b. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 113c. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 113c. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 114c. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 114c. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 115a. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 115a. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 116a. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 116a. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 115b. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 115b. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 116b. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 116b. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 115c. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 115c. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 116c. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 116c. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$.
Data from FigAux 99d. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 99d. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 100d. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 100d. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 99e. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 99e. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 100e. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 100e. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 99f. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 99f. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 100f. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 100f. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 101d. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 101d. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 102d. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 102d. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 101e. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 101e. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 102e. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 102e. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 101f. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 101f. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 102f. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 102f. The full covariance matrices for the all-particle $z_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 103d. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 103d. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 104d. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 104d. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 103e. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 103e. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 104e. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 104e. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 103f. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 103f. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 104f. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 104f. The full covariance matrices for the all-particle $R_g$ distribution for anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 105d. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 105d. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 106d. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 106d. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 105e. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 105e. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 106e. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 106e. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 105f. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 105f. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 106f. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 106f. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 107d. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 107d. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 108d. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 108d. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 107e. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 107e. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 108e. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 108e. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 107f. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 107f. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 108f. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 108f. The full covariance matrices for the all-particle $z_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 109d. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 109d. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 110d. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 110d. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 109e. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 109e. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 110e. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 110e. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 109f. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 109f. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 110f. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 110f. The full covariance matrices for the all-particle $R_g$ distribution for the more central of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 111d. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111d. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112d. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112d. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111e. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 111e. The full covariance matrices for the all-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$.
Data from FigAux 112e. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 112e. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 111f. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 111f. The full covariance matrices for the $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 112f. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 112f. The full covariance matrices for the charged-particle $log_{10}(\rho^2)$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$(resum), measured in the resummation region, $-3.7 < log_{10}(\rho^2) < -1.7$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $log_{10}(\rho^2)$ from -4.5 to -0.5.
Data from FigAux 113d. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 113d. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 114d. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 114d. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 113e. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 113e. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 114e. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 114e. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 113f. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 113f. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 114f. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 114f. The full covariance matrices for the all-particle $z_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 10 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 10 evenly spaced bins in $z_g$ from 0.0 to 0.5.
Data from FigAux 115d. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 115d. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 116d. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 116d. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 0, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 115e. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 115e. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 116e. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 116e. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 1, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 115f. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 115f. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 116f. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
Data from FigAux 116f. The full covariance matrices for the all-particle $R_g$ distribution for the more forward of the two anti-kt R=0.8 jets with $p_T$ > 300 GeV, after the soft drop algorithm is applied for $\beta$ = 2, in data. The distributions are normalized to the integrated cross section, $\sigma$. Each set of 6 bins corresponds to one $p_T$ bin in {300, 400, 600, 800, 1000, infinity } and 6 bins in $r_g$ (0.06310, 0.10000, 0.15849, 0.25119, 0.39811, 0.63096, 0.80000).
A search for supersymmetry through the pair production of electroweakinos with mass splittings near the electroweak scale and decaying via on-shell $W$ and $Z$ bosons is presented for a three-lepton final state. The analyzed proton-proton collision data taken at a center-of-mass energy of $\sqrt{s}$ = 13 TeV were collected between 2015 and 2018 by the ATLAS experiment at the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb$^{-1}$. A search, emulating the recursive jigsaw reconstruction technique with easily reproducible laboratory-frame variables, is performed. The two excesses observed in the 2015-2016 data recursive jigsaw analysis in the low-mass three-lepton phase space are reproduced. Results with the full dataset are in agreement with the Standard Model expectations. They are interpreted to set exclusion limits at 95% confidence level on simplified models of chargino-neutralino pair production for masses up to 345 GeV.
Distributions in SR-low of the data and post-fit background prediction for m<sub>T</sub>. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-low of the data and post-fit background prediction for m<sub>T</sub>. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-low of the data and post-fit background prediction for H<sup>boost</sup>. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-low of the data and post-fit background prediction for H<sup>boost</sup>. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-low of the data and post-fit background prediction for m<sub>eff</sub><sup>3ℓ</sup>/H<sup>boost</sup>. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-low of the data and post-fit background prediction for m<sub>eff</sub><sup>3ℓ</sup>/H<sup>boost</sup>. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-low of the data and post-fit background prediction for p<sub>T</sub><sup>soft</sup>/(p<sub>T</sub><sup>soft</sup> + m<sub>eff</sub><sup>3ℓ</sup>). The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-low of the data and post-fit background prediction for p<sub>T</sub><sup>soft</sup>/(p<sub>T</sub><sup>soft</sup> + m<sub>eff</sub><sup>3ℓ</sup>). The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-ISR of the data and post-fit background prediction for m<sub>T</sub>. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-ISR of the data and post-fit background prediction for m<sub>T</sub>. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-ISR of the data and post-fit background prediction for R(E<sub>T</sub><sup>miss</sup>,jets). The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-ISR of the data and post-fit background prediction for R(E<sub>T</sub><sup>miss</sup>,jets). The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-ISR of the data and post-fit background prediction for p<sub>T</sub><sup>soft</sup>. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-ISR of the data and post-fit background prediction for p<sub>T</sub><sup>soft</sup>. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-ISR of the data and post-fit background prediction for p<sub>T</sub><sup>jets</sup>. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Distributions in SR-ISR of the data and post-fit background prediction for p<sub>T</sub><sup>jets</sup>. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.
Observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Plus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Plus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Minus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Minus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Plus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Plus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Minus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Minus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.
Upper limits on observed wino-bino simplified model signal cross section $\sigma_\text{obs}^\text{95}$.
Upper limits on observed wino-bino simplified model signal cross section $\sigma_\text{obs}^\text{95}$.
Upper limits on expected wino-bino simplified model signal cross section $\sigma_\text{exp}^\text{95}$.
Upper limits on expected wino-bino simplified model signal cross section $\sigma_\text{exp}^\text{95}$.
Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.
Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.
Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.
Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.
Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.
Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.
Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.
Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.
Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.
Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.
Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.
Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.
Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.
Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.
Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.
Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.
The observed and expected yields after the background-only fit in the SRs. The normalization factors of the $WZ$ sample for the low-mass and ISR regions are different and are treated separately in the combined fit. \The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. Combined statistical and systematic uncertainties are presented. The individual uncertainties can be correlated and do not necessarily add in quadrature to equal the total background uncertainty.
The observed and expected yields after the background-only fit in the SRs. The normalization factors of the $WZ$ sample for the low-mass and ISR regions are different and are treated separately in the combined fit. \The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. Combined statistical and systematic uncertainties are presented. The individual uncertainties can be correlated and do not necessarily add in quadrature to equal the total background uncertainty.
Summary of the expected background and data yields in $\text{SR-low}$ and $\text{SR-ISR}$. The second and third columns show the data and total expected background with systematic uncertainties. The fourth column gives the model-independent upper limits at 95\% CL on the visible cross section ($\sigma_\text{vis}$). The fifth and sixth columns give the visible number of observed ($S^{95}_\text{obs}$) and expected ($S^{95}_\text{exp}$) events of a generic beyond-the-SM process, where uncertainties on $S^{95}_\text{exp}$ reflect the $\pm 1 \sigma$ uncertainties on the background estimation. The last column shows the discovery $p$-value and Gaussian significance $Z$ assuming no signal.
Summary of the expected background and data yields in $\text{SR-low}$ and $\text{SR-ISR}$. The second and third columns show the data and total expected background with systematic uncertainties. The fourth column gives the model-independent upper limits at 95\% CL on the visible cross section ($\sigma_\text{vis}$). The fifth and sixth columns give the visible number of observed ($S^{95}_\text{obs}$) and expected ($S^{95}_\text{exp}$) events of a generic beyond-the-SM process, where uncertainties on $S^{95}_\text{exp}$ reflect the $\pm 1 \sigma$ uncertainties on the background estimation. The last column shows the discovery $p$-value and Gaussian significance $Z$ assuming no signal.
Upper limits on observed (expected) wino-bino simplified model signal cross section $\sigma_\text{obs(exp)}^\text{95}$.
Upper limits on observed (expected) wino-bino simplified model signal cross section $\sigma_\text{obs(exp)}^\text{95}$.
Full list of event selections and MC generator-weighted yields and in $\text{SR-ISR}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.
Full list of event selections and MC generator-weighted yields and in $\text{SR-ISR}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.
Full list of event selections and MC generator-weighted yields and in $\text{SR-low}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.
Full list of event selections and MC generator-weighted yields and in $\text{SR-low}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.
This paper describes precision measurements of the transverse momentum $p_\mathrm{T}^{\ell\ell}$ ($\ell=e,\mu$) and of the angular variable $\phi^{*}_{\eta}$ distributions of Drell-Yan lepton pairs in a mass range of 66-116 GeV. The analysis uses data from 36.1 fb$^{-1}$ of proton-proton collisions at a centre-of-mass energy of $\sqrt{s}=13$ TeV collected by the ATLAS experiment at the LHC in 2015 and 2016. Measurements in electron-pair and muon-pair final states are performed in the same fiducial volumes, corrected for detector effects, and combined. Compared to previous measurements in proton-proton collisions at $\sqrt{s}=$7 and 8 TeV, these new measurements probe perturbative QCD at a higher centre-of-mass energy with a different composition of initial states. They reach a precision of 0.2% for the normalized spectra at low values of $p_\mathrm{T}^{\ell\ell}$. The data are compared with different QCD predictions, where it is found that predictions based on resummation approaches can describe the full spectrum within uncertainties.
Selected signal candidate events in data for both decay channels as well as the expected background contributions including their total uncertainties.
Selected signal candidate events in data for both decay channels as well as the expected background contributions including their total uncertainties.
Selected signal candidate events in data for both decay channels as well as the expected background contributions including their total uncertainties.
Overview of the detector efficiency correction factors, $C_{Z}$ , for the electron and muon channels and their systematic uncertainty contributions.
Overview of the detector efficiency correction factors, $C_{Z}$ , for the electron and muon channels and their systematic uncertainty contributions.
Overview of the detector efficiency correction factors, $C_{Z}$ , for the electron and muon channels and their systematic uncertainty contributions.
Measured inclusive cross-section in the fiducial volume in the electron and muon decay channels at Born level and their combination as well as the theory prediction at NNLO in $\alpha_{s}$ using the CT14 PDF set.
Measured inclusive cross-section in the fiducial volume in the electron and muon decay channels at Born level and their combination as well as the theory prediction at NNLO in $\alpha_{s}$ using the CT14 PDF set.
Measured inclusive cross-section in the fiducial volume in the electron and muon decay channels at Born level and their combination as well as the theory prediction at NNLO in $\alpha_{s}$ using the CT14 PDF set.
The measured combined normalized differential cross-sections, divided by the bin-width, in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle level.
The measured combined normalized differential cross-sections, divided by the bin-width, in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle level.
The measured combined normalized differential cross-sections, divided by the bin-width, in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle level.
The measured combined normalized differential cross-sections, divided by the bin-width, in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle level.
The measured combined normalized differential cross-sections, divided by the bin-width, in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle level.
The measured combined normalized differential cross-sections, divided by the bin-width, in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle level.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid}\times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}p_{T}^{ll}$ measured on born level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid}\times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}p_{T}^{ll}$ measured on born level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid}\times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}p_{T}^{ll}$ measured on born level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid}\times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phi_{\eta}^{*}$ measured on born level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid}\times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phi_{\eta}^{*}$ measured on born level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid}\times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phi_{\eta}^{*}$ measured on born level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid}\times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}p_{T}^{ll}$ measured on born level for the $Z\rightarrow ee$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) and Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. for the electron momentum scale and resolution uncertainties; Elec. (Reco), Elec. (ID), Isolation, Trigger and Charge-ID denote the correlated uncertainties of the data/MC scale-factors for the electron reconstruction, identification, isolation, trigger and charge-identification efficiencies; The uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid}\times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}p_{T}^{ll}$ measured on born level for the $Z\rightarrow ee$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) and Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. for the electron momentum scale and resolution uncertainties; Elec. (Reco), Elec. (ID), Isolation, Trigger and Charge-ID denote the correlated uncertainties of the data/MC scale-factors for the electron reconstruction, identification, isolation, trigger and charge-identification efficiencies; The uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid}\times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}p_{T}^{ll}$ measured on born level for the $Z\rightarrow ee$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) and Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. for the electron momentum scale and resolution uncertainties; Elec. (Reco), Elec. (ID), Isolation, Trigger and Charge-ID denote the correlated uncertainties of the data/MC scale-factors for the electron reconstruction, identification, isolation, trigger and charge-identification efficiencies; The uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phi_{\eta}^{*}$ measured on born level for the $Z\rightarrow ee$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) and Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. for the electron momentum scale and resolution uncertainties; Elec. (Reco), Elec. (ID), Isolation, Trigger and Charge-ID denote the correlated uncertainties of the data/MC scale-factors for the electron reconstruction, identification, isolation, trigger and charge-identification efficiencies; The uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phi_{\eta}^{*}$ measured on born level for the $Z\rightarrow ee$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) and Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. for the electron momentum scale and resolution uncertainties; Elec. (Reco), Elec. (ID), Isolation, Trigger and Charge-ID denote the correlated uncertainties of the data/MC scale-factors for the electron reconstruction, identification, isolation, trigger and charge-identification efficiencies; The uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phi_{\eta}^{*}$ measured on born level for the $Z\rightarrow ee$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) and Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. for the electron momentum scale and resolution uncertainties; Elec. (Reco), Elec. (ID), Isolation, Trigger and Charge-ID denote the correlated uncertainties of the data/MC scale-factors for the electron reconstruction, identification, isolation, trigger and charge-identification efficiencies; The uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}p_{T}^{ll}$ measured on bare level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}p_{T}^{ll}$ measured on bare level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}p_{T}^{ll}$ measured on bare level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phi_{\eta}^{*}$ measured on bare level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phi_{\eta}^{*}$ measured on bare level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Results of the normalized differential cross-section $1/\sigma_\mathrm{fid} \times \mathrm{d}\sigma_\mathrm{fid}/\mathrm{d}\phi_{\eta}^{*}$ measured on bare level for the $Z\rightarrow\mu\mu$ decay channel. The following naming convention is used: Stat.(Data), Stat.(MC) an Eff.(Uncor.), denote the statistical uncertainties due limited data and MC as well as the uncorrelated lepton efficiency uncertainties; Scale and Res. denote the muon momentum scale and resolution uncertainties; Muon Sag. denotes the uncertainty due to the muon sagitta bias; Eff. (Cor.), Isolation, Trigger and TTVA denote the uncertainties of the data/MC scale-factors for the correlated muon reconstruction, isolation, trigger and track-to-vertex matching efficiencies; the uncertainties due to the primary vertex z-distribution and pile-up reweighting are denoted as Z-Pos and Pile-Up, while the model and background uncertainties are summarized under Model and Bkg.. The sign-information is kept to track bin-to-bin changes.
Measured combined normalized differential cross-section in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle.
Measured combined normalized differential cross-section in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle.
Measured combined normalized differential cross-section in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle.
Measured combined normalized differential cross-section in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle.
Measured combined normalized differential cross-section in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle.
Measured combined normalized differential cross-section in the fiducial volume at Born level as well as a factor $k_{dressed}$ to translate from the Born particle level to the dressed particle.
The distribution of events passing the selection requirements in the electron channel as a function of dilepton invariant mass $m_{ll}$ , the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of dilepton invariant mass $m_{ll}$ , the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of dilepton invariant mass $m_{ll}$ , the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of dilepton invariant mass $m_{ll}$, the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of dilepton invariant mass $m_{ll}$, the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of dilepton invariant mass $m_{ll}$, the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of lepton pseudorapidity $\eta$, the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of lepton pseudorapidity $\eta$, the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of lepton pseudorapidity $\eta$, the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of lepton pseudorapidity $\eta$, the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of lepton pseudorapidity $\eta$, the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of lepton pseudorapidity $\eta$, the latter with one entry for each lepton per event. The MC signal sample is simulated using Powheg+Pythia8. The predictions of the MC signal sample together with the MC background samples are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of dilepton transverse momentum. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of dilepton transverse momentum. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of dilepton transverse momentum. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of dilepton transverse momentum. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of dilepton transverse momentum. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of dilepton transverse momentum. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of $\phi_{\eta}^{*}$. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of $\phi_{\eta}^{*}$. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the electron channel as a function of $\phi_{\eta}^{*}$. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of $\phi_{\eta}^{*}$. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of $\phi_{\eta}^{*}$. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The distribution of events passing the selection requirements in the muon channel as a function of $\phi_{\eta}^{*}$. The MC signal sample is simulated using Powheg+Pythia8. The predictions are normalized to the integral of the data and the total experimental uncertainty of the predicted values is shown as a grey band in the ratio of the prediction to data.
The measured normalized cross section as a function of $p_{ll}$ for the electron and muon channels and the combined result as well as their ratio together with the total uncertainties, shown as a blue band. The pull distribution between the electron and muon channels, defined as the difference between the two channels divided by the combined uncorrelated uncertainty, is also shown. The $p_{ll}$ distribution is split into linear and logarithmic scales at 30 GeV.
The measured normalized cross section as a function of $p_{ll}$ for the electron and muon channels and the combined result as well as their ratio together with the total uncertainties, shown as a blue band. The pull distribution between the electron and muon channels, defined as the difference between the two channels divided by the combined uncorrelated uncertainty, is also shown. The $p_{ll}$ distribution is split into linear and logarithmic scales at 30 GeV.
The measured normalized cross section as a function of $p_{ll}$ for the electron and muon channels and the combined result as well as their ratio together with the total uncertainties, shown as a blue band. The pull distribution between the electron and muon channels, defined as the difference between the two channels divided by the combined uncorrelated uncertainty, is also shown. The $p_{ll}$ distribution is split into linear and logarithmic scales at 30 GeV.
The measured normalized cross section as a function of $\phi_{\eta}^{*}$ for the electron and muon channels and the combined result as well as their ratio together with the total uncertainties, shown as a blue band. The pull distribution between the electron and muon channels, defined as the difference between the two channels divided by the combined uncorrelated uncertainty, is also shown.
The measured normalized cross section as a function of $\phi_{\eta}^{*}$ for the electron and muon channels and the combined result as well as their ratio together with the total uncertainties, shown as a blue band. The pull distribution between the electron and muon channels, defined as the difference between the two channels divided by the combined uncorrelated uncertainty, is also shown.
The measured normalized cross section as a function of $\phi_{\eta}^{*}$ for the electron and muon channels and the combined result as well as their ratio together with the total uncertainties, shown as a blue band. The pull distribution between the electron and muon channels, defined as the difference between the two channels divided by the combined uncorrelated uncertainty, is also shown.
Comparison of the normalized $p_{ll}$ distributions predicted by different computations: Pythia8 with the AZ tune, Powheg+Pythia8 with the AZNLO tune, Sherpa v2.2.1 and RadISH with the Born level combined measurement. The uncertainties of the measurement are shown as vertical bars and uncertainties of the Sherpa and RadISH predictions are indicated by the coloured bands.
Comparison of the normalized $p_{ll}$ distributions predicted by different computations: Pythia8 with the AZ tune, Powheg+Pythia8 with the AZNLO tune, Sherpa v2.2.1 and RadISH with the Born level combined measurement. The uncertainties of the measurement are shown as vertical bars and uncertainties of the Sherpa and RadISH predictions are indicated by the coloured bands.
Comparison of the normalized $p_{ll}$ distributions predicted by different computations: Pythia8 with the AZ tune, Powheg+Pythia8 with the AZNLO tune, Sherpa v2.2.1 and RadISH with the Born level combined measurement. The uncertainties of the measurement are shown as vertical bars and uncertainties of the Sherpa and RadISH predictions are indicated by the coloured bands.
Comparison of the normalized $\phi_{\eta}^{*}$ distributions predicted by different computations: Pythia8 with the AZ tune, Powheg+Pythia8 with the AZNLO tune, Sherpa v2.2.1 and RadISH with the Born level combined measurement. The uncertainties of the measurement are shown as vertical bars and uncertainties of the Sherpa and RadISH predictions are indicated by the coloured bands.
Comparison of the normalized $\phi_{\eta}^{*}$ distributions predicted by different computations: Pythia8 with the AZ tune, Powheg+Pythia8 with the AZNLO tune, Sherpa v2.2.1 and RadISH with the Born level combined measurement. The uncertainties of the measurement are shown as vertical bars and uncertainties of the Sherpa and RadISH predictions are indicated by the coloured bands.
Comparison of the normalized $\phi_{\eta}^{*}$ distributions predicted by different computations: Pythia8 with the AZ tune, Powheg+Pythia8 with the AZNLO tune, Sherpa v2.2.1 and RadISH with the Born level combined measurement. The uncertainties of the measurement are shown as vertical bars and uncertainties of the Sherpa and RadISH predictions are indicated by the coloured bands.
Comparison of the normalized $p_{ll}$ distribution in the range $p_{ll}$ > 10 GeV. The Born level combined measurement is compared with predictions by Sherpa v2.2.1, fixed-order NNLOjet and NNLOjet supplied with NLO electroweak corrections. The uncertainties in the measurement are shown as vertical bars and the uncertainties in the predictions are indicated by the coloured bands.
Comparison of the normalized $p_{ll}$ distribution in the range $p_{ll}$ > 10 GeV. The Born level combined measurement is compared with predictions by Sherpa v2.2.1, fixed-order NNLOjet and NNLOjet supplied with NLO electroweak corrections. The uncertainties in the measurement are shown as vertical bars and the uncertainties in the predictions are indicated by the coloured bands.
Comparison of the normalized $p_{ll}$ distribution in the range $p_{ll}$ > 10 GeV. The Born level combined measurement is compared with predictions by Sherpa v2.2.1, fixed-order NNLOjet and NNLOjet supplied with NLO electroweak corrections. The uncertainties in the measurement are shown as vertical bars and the uncertainties in the predictions are indicated by the coloured bands.
The measured combined normalized differential cross-sections, divided by the bin-width, in the fiducial volume at dressed level.
The measured combined normalized differential cross-sections, divided by the bin-width, in the fiducial volume at dressed level.
A search is presented for pair-production of long-lived neutral particles using 33 fb$^{-1}$ of $\sqrt{s} = 13$ TeV proton-proton collision data, collected during 2016 by the ATLAS detector at the LHC. This search focuses on a topology in which one long-lived particle decays in the ATLAS inner detector and the other decays in the muon spectrometer. Special techniques are employed to reconstruct the displaced tracks and vertices in the inner detector and in the muon spectrometer. One event is observed that passes the full event selection, which is consistent with the estimated background. Limits are placed on scalar boson propagators with masses from 125 GeV to 1000 GeV decaying into pairs of long-lived hidden-sector scalars with masses from 8 GeV to 400 GeV. The limits placed on several low-mass scalars extend previous exclusion limits in the range of proper lifetimes $c \tau$ from 5 cm to 1 m.
IDVx selection efficiency as a function of the radial decay position for $m_H = 125$ GeV.
IDVx selection efficiency as a function of the radial decay position for $m_s = 50$ GeV.
Observed $CL_S$ limits on $BR$ for $m_H = 125$ GeV.
Observed $CL_S$ limits on $\sigma \times BR$ for $m_{\Phi} = 200-400$ GeV.
Observed $CL_S$ limits on $\sigma \times BR$ for $m_{\Phi} = 600-1000$ GeV.
Combined limits from this analysis (ID) and the CR and MS analyses for $m_{H} = 125$ GeV, $m_s = 15$ GeV.
Combined limits from this analysis (ID) and the CR and MS analyses for $m_{H} = 125$ GeV, $m_s = 25$ GeV.
Combined limits from this analysis (ID) and the CR and MS analyses for $m_{H} = 125$ GeV, $m_s = 40$ GeV.
Combined limits from this analysis (ID) and the CR and MS analyses for $m_{H} = 125$ GeV, $m_s = 55$ GeV.
Combined limits from this analysis (ID) and the CR and MS analyses for $m_{\Phi} = 200$ GeV, $m_s = 25$ GeV.
Combined limits from this analysis (ID) and the CR and MS analyses for $m_{\Phi} = 200$ GeV, $m_s = 50$ GeV.
Comparison of the IDVx reconstruction and selection efficiency for a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV, using only standard tracking versus using standard and large radius tracking for all ID vertices in the signal MC sample.
Comparison of the IDVx reconstruction and selection efficiency for a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV, using only standard tracking versus using standard and large radius tracking for ID vertices passing the full IDVx selection criteria.
Comparison of the IDVx reconstruction and selection efficiency for a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV, using only standard tracking versus using standard and large radius tracking for all ID vertices in the signal MC sample.
Comparison of the IDVx reconstruction and selection efficiency for a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV, using only standard tracking versus using standard and large radius tracking for ID vertices passing the full IDVx selection criteria.
The IDVx selection efficiency as a function of long-lived particle decay $z$ position for MC signal samples with a 125 GeV Higgs boson decaying to LLPs with masses of 8, 25, and 55 GeV. The efficiency for the 55 GeV LLP sample is shown both with and without the material veto applied.
The IDVx selection efficiency as a function of long-lived particle decay $z$ position for MC signal samples with mediators of masses 200, 400, and 600 GeV decaying to LLPs with a mass of 50 GeV. The efficiency for the 200 GeV mediator sample is shown both with and without the material veto applied.
The impact of the IDVx selections on the selection efficiency for the MC signal sample with a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV.
The impact of the IDVx selections on the selection efficiency for the MC signal sample with a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV.
The impact of the IDVx selections on the selection efficiency for the MC signal sample with a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV.
The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 400 GeV decaying to an LLP with a mass of 50 GeV.
The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 400 GeV decaying to an LLP with a mass of 50 GeV.
The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 400 GeV decaying to an LLP with a mass of 50 GeV.
The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV.
The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV.
The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV.
$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 8$ GeV.
$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 15$ GeV.
$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 25$ GeV.
$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 40$ GeV.
$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 55$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 200$ GeV, $m_s = 8$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 200$ GeV, $m_s = 25$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 200$ GeV, $m_s = 50$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 400$ GeV, $m_s = 50$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 400$ GeV, $m_s = 100$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 600$ GeV, $m_s = 50$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 600$ GeV, $m_s = 150$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 1000$ GeV, $m_s = 50$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 1000$ GeV, $m_s = 150$ GeV.
$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 1000$ GeV, $m_s = 400$ GeV.
A search for the direct production of the supersymmetric partners of $\tau$-leptons (staus) in final states with two hadronically decaying $\tau$-leptons is presented. The analysis uses a dataset of $pp$ collisions corresponding to an integrated luminosity of $139$ fb$^{-1}$, recorded with the ATLAS detector at the Large Hadron Collider at a center-of-mass energy of 13 TeV. No significant deviation from the expected Standard Model background is observed. Limits are derived in scenarios of direct production of stau pairs with each stau decaying into the stable lightest neutralino and one $\tau$-lepton in simplified models where the two stau mass eigenstates are degenerate. Stau masses from 120 GeV to 390 GeV are excluded at 95% confidence level for a massless lightest neutralino.
The observed upper limits on the model cross-section in units of pb for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production. Three points at ${M({\tilde{\chi}}^{0}_{1})}=200GeV$ were removed from the plot but kept in the table because they overlapped with the plot's legend and are far from the exclusion contour.
The observed upper limits on the model cross-section in units of pb for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production. Three points at ${M({\tilde{\chi}}^{0}_{1})}=200GeV$ were removed from the plot but kept in the table because they overlapped with the plot's legend and are far from the exclusion contour.
The observed upper limits on the model cross-section in units of pb for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production. Three points at $M({\tilde{\chi}}^{0}_{1})=200GeV$ were removed from the plot but kept in the table because they overlapped with the plot's legend and are far from the exclusion contour.
The observed upper limits on the model cross-section in units of pb for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production. Three points at $M({\tilde{\chi}}^{0}_{1})=200GeV$ were removed from the plot but kept in the table because they overlapped with the plot's legend and are far from the exclusion contour.
The observed 95\% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.
The observed 95\% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.
The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.
The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.
The observed 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.
The observed 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.
The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.
The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.
Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.
Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.
Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.
Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.
Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.
Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.
Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.
Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.
Signal acceptance in SR highMass for combined stau final states
Signal acceptance in SR highMass for combined stau final states
Signal acceptance in SR lowMass for combined stau final states
Signal acceptance in SR lowMass for combined stau final states
Signal efficiency in SR highMass for combined stau final states
Signal efficiency in SR highMass for combined stau final states
Signal efficiency in SR lowMass for combined stau final states
Signal efficiency in SR lowMass for combined stau final states
Signal acceptance*efficiency in SR highMass for combined stau final states
Signal acceptance*efficiency in SR highMass for combined stau final states
Signal acceptance*efficiency in SR lowMass for combined stau final states
Signal acceptance*efficiency in SR lowMass for combined stau final states
Cutflow for two reference points (${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production) in SR. The column labelled $N_{weighted}$ shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$, while $N_{raw}$ in brackets shows the results for the generated number of events. The quoted uncertainties are statistical only. The "Generator filter" includes the requirements that two $\tau$ in the event have ${p}_{T} > 15$ GeV and $|\eta| <$ 2.6. The "Baseline Cut" includes the requirement of two baseline $\tau$ with a minimum value at 0.01 of the boosted decision tree discriminant (JetBDTSigTransMin $>$ 0.01) and ${p}_{T, \tau_{1}} > 50$ GeV and ${p}_{T, \tau_{2}} > 40$ GeV. At the step "Trigger & offline cuts", the following requirements are applied: the event is recorded using the asymmetric di-$\tau$ trigger (di-$\tau$ $E_{T}^{miss}$ trigger) in SR-lowMass (SR-highMass), and the lepton $p_{T}$ and $E_{T}^{miss}$ are required at plateau.
Cutflow for two reference points (${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production) in SR. The column labelled $N_{weighted}$ shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$, while $N_{raw}$ in brackets shows the results for the generated number of events. The quoted uncertainties are statistical only. The "Generator filter" includes the requirements that two $\tau$ in the event have ${p}_{T} > 15$ GeV and $|\eta| <$ 2.6. The "Baseline Cut" includes the requirement of two baseline $\tau$ with a minimum value at 0.01 of the boosted decision tree discriminant (JetBDTSigTransMin $>$ 0.01) and ${p}_{T, \tau_{1}} > 50$ GeV and ${p}_{T, \tau_{2}} > 40$ GeV. At the step "Trigger & offline cuts", the following requirements are applied: the event is recorded using the asymmetric di-$\tau$ trigger (di-$\tau$ $E_{T}^{miss}$ trigger) in SR-lowMass (SR-highMass), and the lepton $p_{T}$ and $E_{T}^{miss}$ are required at plateau.
Observed and expected numbers of events in the control and signal regions where all control and signal region bins are included as constraints in the likelihood. The expected event yields of SM processes are given after the background-only fit. The entries marked as "--" are negligible. The uncertainties correspond to the sum in quadrature of statistical and systematic uncertainties. The correlation of systematic uncertainties among control regions and among background processes is fully taken into account.
Observed and expected numbers of events in the control and signal regions where all control and signal region bins are included as constraints in the likelihood. The expected event yields of SM processes are given after the background-only fit. The entries marked as "--" are negligible. The uncertainties correspond to the sum in quadrature of statistical and systematic uncertainties. The correlation of systematic uncertainties among control regions and among background processes is fully taken into account.
The post-fit $m_{T2}$ distribution for SR-lowMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.
The post-fit $m_{T2}$ distribution for SR-lowMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.
The post-fit $m_{T2}$ distribution for SR-highMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.
The post-fit $m_{T2}$ distribution for SR-highMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.
The $m_{T2}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The $m_{T2}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The $m_{T2}$ post-fit distributions in the multi-jet background validation VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The $m_{T2}$ post-fit distributions in the multi-jet background validation VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The pre-fit $m_{T2}$ distribution in the $WCR$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is estimated from data using the OS--SS method. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The pre-fit $m_{T2}$ distribution in the $WCR$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is estimated from data using the OS--SS method. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.
The post-fit yields in the $WVR$, $TVRs$, $ZVRs$ and $VVVRs$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is negligible and is estimated from data using the ABCD method, using CRs obtained with the same technique used for the SRs. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines. The lower panels show the ratio of data to the SM background estimate.
The post-fit yields in the $WVR$, $TVRs$, $ZVRs$ and $VVVRs$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is negligible and is estimated from data using the ABCD method, using CRs obtained with the same technique used for the SRs. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines. The lower panels show the ratio of data to the SM background estimate.
The production of a prompt photon in association with a $Z$ boson is studied in proton-proton collisions at a centre-of-mass energy $\sqrt{s} =$ 13 TeV. The analysis uses a data sample with an integrated luminosity of 139 fb$^{-1}$ collected by the ATLAS detector at the LHC from 2015 to 2018. The production cross-section for the process $pp \rightarrow \ell^+\ell^-\gamma+X$ ($\ell = e, \mu$) is measured within a fiducial phase-space region defined by kinematic requirements on the photon and the leptons, and by isolation requirements on the photon. An experimental precision of 2.9% is achieved for the fiducial cross-section. Differential cross-sections are measured as a function of each of six kinematic variables characterising the $\ell^+\ell^-\gamma$ system. The data are compared with theoretical predictions based on next-to-leading-order and next-to-next-to-leading-order perturbative QCD calculations. The impact of next-to-leading-order electroweak corrections is also considered.
The measured fiducial cross section. "Uncor" uncertainty includes all systematic uncertainties that are uncorrelated between electron and muon channels such as the uncertainty on the electron identification efficiency and the uncorrelated component of the background uncertainties. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with SHERPA 2.2.2 at NLO. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production of 4.57 fb.
The measured fiducial cross section. "Uncor" uncertainty includes all systematic uncertainties that are uncorrelated between electron and muon channels such as the uncertainty on the electron identification efficiency and the uncorrelated component of the background uncertainties. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with Sherpa 2.2.2 at NLO. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production of 4.57 fb.
The measured fiducial cross section vs $E_{\mathrm{T}}^\gamma$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with SHERPA 2.2.2 at NLO. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $E_{\mathrm{T}}^\gamma$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with Sherpa 2.2.2 at NLO. The uncertainty is defined as Max(stat error, systematic difference between Sherpa LO and Sherpa 2.2.2 NLO), and cannot be considered correlated bin-to-bin. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $|\eta^\gamma|$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with SHERPA 2.2.2 at NLO. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $|\eta^\gamma|$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with Sherpa 2.2.2 at NLO. The uncertainty is defined as Max(stat error, systematic difference between Sherpa LO and Sherpa 2.2.2 NLO), and cannot be considered correlated bin-to-bin. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $m(\ell\ell\gamma)$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with SHERPA 2.2.2 at NLO. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $m(\ell\ell\gamma)$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with Sherpa 2.2.2 at NLO. The uncertainty is defined as Max(stat error, systematic difference between Sherpa LO and Sherpa 2.2.2 NLO), and cannot be considered correlated bin-to-bin. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $p_{\mathrm{T}}^{\ell\ell\gamma}$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with SHERPA 2.2.2 at NLO. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $p_{\mathrm{T}}^{\ell\ell\gamma}$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with Sherpa 2.2.2 at NLO. The uncertainty is defined as Max(stat error, systematic difference between Sherpa LO and Sherpa 2.2.2 NLO), and cannot be considered correlated bin-to-bin. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $\Delta\phi(ll,\gamma)$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with SHERPA 2.2.2 at NLO. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $\Delta\phi(ll,\gamma)$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with Sherpa 2.2.2 at NLO. The uncertainty is defined as Max(stat error, systematic difference between Sherpa LO and Sherpa 2.2.2 NLO), and cannot be considered correlated bin-to-bin. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $p_{\mathrm{T}}^{\ell\ell\gamma}/m(\ell\ell\gamma)$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with SHERPA 2.2.2 at NLO. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
The measured fiducial cross section vs $p_{\mathrm{T}}^{\ell\ell\gamma}/m(\ell\ell\gamma)$. The central values are provided along with the statistical and systematic uncertainties together with the sign information. The statistical and "Uncor" uncertainty should be treated as uncorrelated bin-to-bin, while the rest are correlated between bins, and they are written as signed NP variations. The parton-to-particle correction factor $C_{theory}$ is the ratio of the cross-section predicted by Sherpa LO samples at particle level within the fiducial phase-space region defined in Table 4 to the predicted cross-section at parton level within the same fiducial region but with the smooth-cone isolation prescription defined above replacing the particle-level photon isolation criterion, and with Born-level leptons in place of dressed leptons. This correction should be applied on fixed order parton-level calculations. The systematic uncertainty is evaluated from a comparison with the correction factor obtained using events generated with Sherpa 2.2.2 at NLO. The uncertainty is defined as Max(stat error, systematic difference between Sherpa LO and Sherpa 2.2.2 NLO), and cannot be considered correlated bin-to-bin. In the case that the calculations are valid for dressed leptons, a modified correction factor excluding the Born-to-dressed lepton correction should be applied instead. This correction only takes into account the particle-level isolation criteria, and is provided separately here. The Sherpa 2.2.8 NLO cross-sections given below include a small contribution from EW $Z\gamma jj$ production.
A dedicated sample of Large Hadron Collider proton-proton collision data at centre-of-mass energy $\sqrt{s}=8$ TeV is used to study inclusive single diffractive dissociation, $pp \rightarrow Xp$. The intact final-state proton is reconstructed in the ATLAS ALFA forward spectrometer, while charged particles from the dissociated system $X$ are measured in the central detector components. The fiducial range of the measurement is $-4.0 < \log_{10} \xi < -1.6$ and $0.016 < |t| < 0.43 \ {\rm GeV^2}$, where $\xi$ is the proton fractional energy loss and $t$ is the squared four-momentum transfer. The total cross section integrated across the fiducial range is $1.59 \pm 0.13 \ {\rm mb}$. Cross sections are also measured differentially as functions of $\xi$, $t$, and $\Delta \eta$, a variable that characterises the rapidity gap separating the proton and the system $X$. The data are consistent with an exponential $t$ dependence, ${\rm d} \sigma / {\rm d} t \propto \text{e}^{Bt}$ with slope parameter $B = 7.65 \pm 0.34 \ {\rm GeV^{-2}}$. Interpreted in the framework of triple Regge phenomenology, the $\xi$ dependence leads to a pomeron intercept of $\alpha(0) = 1.07 \pm 0.09$.
Hadron-level differential SD cross section as a function of Delta Eta.
Hadron-level differential SD cross section as a function of t.
Hadron-level differential SD cross section as a function of log_10 xi.
The inclusive top quark pair ($t\bar{t}$) production cross-section $\sigma_{t\bar{t}}$ has been measured in proton$-$proton collisions at $\sqrt{s}=13$ TeV, using $36.1$ fb$^{-1}$ of data collected in 2015$-$16 by the ATLAS experiment at the LHC. Using events with an opposite-charge $e\mu$ pair and $b$-tagged jets, the cross-section is measured to be: \begin{equation}\nonumber \sigma_{t\bar{t}} = 826.4 \pm 3.6\,\mathrm{(stat)}\ \pm 11.5\,\mathrm{(syst)}\ \pm 15.7\,\mathrm{(lumi)}\ \pm 1.9\,\mathrm{(beam)}\,\mathrm{pb}, \end{equation} where the uncertainties reflect the limited size of the data sample, experimental and theoretical systematic effects, the integrated luminosity, and the LHC beam energy, giving a total uncertainty of 2.4%. The result is consistent with theoretical QCD calculations at next-to-next-to-leading order. It is used to determine the top quark pole mass via the dependence of the predicted cross-section on $m_t^{\mathrm{pole}}$, giving $m_t^{\mathrm{pole}}=173.1^{+2.0}_{-2.1}$ GeV. It is also combined with measurements at $\sqrt{s}=7$ TeV and $\sqrt{s}=8$ TeV to derive ratios and double ratios of $t\bar{t}$ and $Z$ cross-sections at different energies. The same event sample is used to measure absolute and normalised differential cross-sections as functions of single-lepton and dilepton kinematic variables, and the results compared with predictions from various Monte Carlo event generators.
Absolute differential cross-section in the fiducial region as a function of lepton pT. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 23 and 24.
Normalised differential cross-section in the fiducial region as a function of lepton pT. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 25 and 26.
Absolute differential cross-section in the fiducial region as a function of lepton |eta|. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The corresponding correlation matrices are given in Tables 27 and 28.
Normalised differential cross-section in the fiducial region as a function of lepton |eta|. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The corresponding correlation matrices are given in Tables 29 and 30.
Absolute differential cross-section in the fiducial region as a function of dilepton pT. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 31 and 32.
Normalised differential cross-section in the fiducial region as a function of dilepton pT. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 33 and 34.
Absolute differential cross-section in the fiducial region as a function of dilepton invariant mass. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 35 and 36.
Normalised differential cross-section in the fiducial region as a function of dilepton invariant mass. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 37 and 38.
Absolute differential cross-section in the fiducial region as a function of dilepton |rapidity|. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The corresponding correlation matrices are given in Tables 39 and 40.
Normalised differential cross-section in the fiducial region as a function of dilepton |rapidity|. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The corresponding correlation matrices are given in Tables 41 and 42.
Absolute differential cross-section in the fiducial region as a function of the azimuthal angle between the leptons. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The corresponding correlation matrices are given in Tables 43 and 44.
Normalised differential cross-section in the fiducial region as a function of the azimuthal angle between the leptons. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The corresponding correlation matrices are given in Tables 45 and 46.
Absolute differential cross-section in the fiducial region as a function of the sum of pT of the two leptons. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 47 and 48.
Normalised differential cross-section in the fiducial region as a function of the sum of pT of the two leptons. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 49 and 50.
Absolute differential cross-section in the fiducial region as a function of the sum of the energies of the two leptons. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 51 and 52.
Normalised differential cross-section in the fiducial region as a function of the sum of the energies of the two leptons. The first column gives the cross-section including contributions from leptonic tau decays, the second without. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb). The last bin includes overflow beyond the upper bin boundary. The corresponding correlation matrices are given in Tables 53 and 54.
Absolute differential cross-section in the fiducial region as a function of lepton |eta| and dilepton invariant mass. The first four columns give the cross-section including contributions from leptonic tau decays in the four mass bins, and the last four columns do not include the leptonic tau contributions. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb).
Normalised differential cross-section in the fiducial region as a function of lepton |eta| and dilepton invariant mass. The first four columns give the cross-section including contributions from leptonic tau decays in the four mass bins, and the last four columns do not include the leptonic tau contributions. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb).
Absolute differential cross-section in the fiducial region as a function of dilepton |rapidity| and dilepton invariant mass. The first four columns give the cross-section including contributions from leptonic tau decays in the four mass bins, and the last four columns do not include the leptonic tau contributions. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb).
Normalised differential cross-section in the fiducial region as a function of dilepton |rapidity| and dilepton invariant mass. The first four columns give the cross-section including contributions from leptonic tau decays in the four mass bins, and the last four columns do not include the leptonic tau contributions. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb).
Absolute differential cross-section in the fiducial region as a function of the azimuthal angle between the leptons and dilepton invariant mass. The first four columns give the cross-section including contributions from leptonic tau decays in the four mass bins, and the last four columns do not include the leptonic tau contributions. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb).
Normalised differential cross-section in the fiducial region as a function of the azimuthal angle between the leptons and dilepton invariant mass. The first four columns give the cross-section including contributions from leptonic tau decays in the four mass bins, and the last four columns do not include the leptonic tau contributions. Systematic uncertainties are given for ttbar modelling (ttmod), lepton calibration (lept), jet and b-tagging calibration (jet), backgrounds (bkg) and integrated luminosity and beam energy (leb).
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of lepton pT as measured in Table 1, including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of lepton pT as measured in Table 1, not including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of lepton pT as measured in Table 2, including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of lepton pT as measured in Table 2, not including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of lepton |eta| as measured in Table 3, including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of lepton |eta| as measured in Table 3, not including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of lepton |eta| as measured in Table 4, including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of lepton |eta| as measured in Table 4, not including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of dilepton pT as measured in Table 5, including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of dilepton pT as measured in Table 5, not including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of dilepton pT as measured in Table 6, including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of dilepton pT as measured in Table 6, not including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of dilepton invariant mass as measured in Table 7, including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of dilepton invariant mass as measured in Table 7, not including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of dilepton invariant mass as measured in Table 8, including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of dilepton invariant mass as measured in Table 8, not including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of dilepton |rapidity| as measured in Table 9, including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of dilepton |rapidity| as measured in Table 9, not including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of dilepton |rapidity| as measured in Table 10, including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of dilepton |rapidity| as measured in Table 10, not including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of the azimuthal angle between the leptons as measured in Table 11, including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of the azimuthal angle between the leptons as measured in Table 11, not including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of the azimuthal angle between the leptons as measured in Table 12, including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of the azimuthal angle between the leptons as measured in Table 12, not including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of the sum of pT of the two leptons as measured in Table 13, including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of the sum of pT of the two leptons as measured in Table 13, not including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of the sum of pT of the two leptons as measured in Table 14, including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of the sum of pT of the two leptons as measured in Table 14, not including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of the sum of the energies of the two leptons as measured in Table 15, including tau decay contributions.
Correlation matrix for the absolute differential cross-section in the fiducial region as a function of the sum of the energies of the two leptons as measured in Table 15, not including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of the sum of the energies of the two leptons as measured in Table 16, including tau decay contributions.
Correlation matrix for the normalised differential cross-section in the fiducial region as a function of the sum of the energies of the two leptons as measured in Table 16, not including tau decay contributions.
Correlation matrix for the combination of all eight one-dimensional absolute fiducial cross-section measurements, including tau decay contributions. The observables are arranged as follows: lepton pT (bins 1-11),lepton |eta| (bins 12-20),dilepton pT (bins 21-29),dilepton invariant mass (bins 30-41),dilepton |rapidity| (bins 42-50),the azimuthal angle between the leptons (bins 51-60),the sum of pT of the two leptons (bins 61-68),the sum of the energies of the two leptons (bins 69-78).
Correlation matrix for the combination of all eight one-dimensional absolute fiducial cross-section measurements, not including tau decay contributions. The observables are arranged as follows: lepton pT (bins 1-11),lepton |eta| (bins 12-20),dilepton pT (bins 21-29),dilepton invariant mass (bins 30-41),dilepton |rapidity| (bins 42-50),the azimuthal angle between the leptons (bins 51-60),the sum of pT of the two leptons (bins 61-68),the sum of the energies of the two leptons (bins 69-78).
Correlation matrix for the combination of all eight one-dimensional normalised fiducial cross-section measurements, including tau decay contributions. The observables are arranged as follows: lepton pT (bins 1-10),lepton |eta| (bins 11-18),dilepton pT (bins 19-26),dilepton invariant mass (bins 27-37),dilepton |rapidity| (bins 38-45),the azimuthal angle between the leptons (bins 46-54),the sum of pT of the two leptons (bins 55-61),the sum of the energies of the two leptons (bins 62-70).
Correlation matrix for the combination of all eight one-dimensional normalised fiducial cross-section measurements, not including tau decay contributions. The observables are arranged as follows: lepton pT (bins 1-10),lepton |eta| (bins 11-18),dilepton pT (bins 19-26),dilepton invariant mass (bins 27-37),dilepton |rapidity| (bins 38-45),the azimuthal angle between the leptons (bins 46-54),the sum of pT of the two leptons (bins 55-61),the sum of the energies of the two leptons (bins 62-70).
Fiducial region definition
A search for new resonances decaying into a pair of jets is reported using the dataset of proton-proton collisions recorded at $\sqrt{s}=13$ TeV with the ATLAS detector at the Large Hadron Collider between 2015 and 2018, corresponding to an integrated luminosity of 139 fb$^{-1}$. The distribution of the invariant mass of the two leading jets is examined for local excesses above a data-derived estimate of the Standard Model background. In addition to an inclusive dijet search, events with jets identified as containing $b$-hadrons are examined specifically. No significant excess of events above the smoothly falling background spectra is observed. The results are used to set cross-section upper limits at 95% confidence level on a range of new physics scenarios. Model-independent limits on Gaussian-shaped signals are also reported. The analysis looking at jets containing $b$-hadrons benefits from improvements in the jet flavour identification at high transverse momentum, which increases its sensitivity relative to the previous analysis beyond that expected from the higher integrated luminosity.
The probability of an event to pass the b-tagging requirement after the rest of the event selection, shown as a function of the resonance mass and for the 1b and 2b analysis categories.
Dijet invariant mass distribution for the inclusive category with |y*| < 0.6.
Dijet invariant mass distribution for the inclusive category with |y*| < 1.2.
Dijet invariant mass distribution for the category with at least one b-tagged jet.
Dijet invariant mass distribution for the category with both jets b-tagged.
The 95% CL upper limits on the cross-section times acceptance times branching ratio into two jets as a function of the mass of q* signal.
The 95% CL upper limits on the cross-section times acceptance times branching ratio into two jets as a function of the mass of QBH signal.
The 95% CL upper limits on the cross-section times acceptance times branching ratio into two jets as a function of the mass of W' signal.
The 95% CL upper limits on the cross-section times acceptance times branching ratio into two jets as a function of the mass of W* signal.
The upper limits on the DM mediator Z' signal at 95% CL from the inclusive category. The 95% CL upper limits are set on the universal quark coupling $g_q$ as a function of the Z' mass.
The 95% CL upper limit on the cross-section times acceptance times b-tagging efficiency times branching ratio as a function of the mass of the b* signal.
The 95% CL upper limit on the cross-section times acceptance times b-tagging efficiency times branching ratio as a function of the signal mass in the DM mediator Z' with $g_q$ = 0.25 model.
The 95% CL upper limit on the cross-section times acceptance times b-tagging efficiency times branching ratio as a function of the signal mass in the SSM Z' model.
The 95% CL upper limit on the cross-section times acceptance times b-tagging efficiency times branching ratio as a function of the signal mass in the graviton with $k/\overline{M}_{PL}=0.2$ model.
The 95% CL upper limit on the cross-section times kinematic acceptance times branching ratio for resonances with a generic Gaussian shape, as a function of the Gaussian mean mass in the inclusive category. Different widths, from 0% up to 15% of the signal mass, are considered. Gaussian-shape signals with 0% widths correspond to signal widths smaller than the experimental resolution. For a Gaussian-shaped signal with a relative width of 15%, the limits are truncated at high mass when the broad signal starts to overlap the upper end of the mass spectrum.
The 95% CL upper limit on the cross-section times kinematic acceptance times b-tagging efficiency times branching ratio for resonances with a generic Gaussian shape, as a function of the Gaussian mean mass in the 1 b category. Different widths, from 0% up to 15% of the signal mass, are considered. Gaussian-shape signals with 0% widths correspond to signal widths smaller than the experimental resolution. For a Gaussian-shaped signal with a relative width of 15%, the limits are truncated at high mass when the broad signal starts to overlap the upper end of the mass spectrum.
The 95% CL upper limit on the cross-section times kinematic acceptance times b-tagging efficiency times branching ratio for resonances with a generic Gaussian shape, as a function of the Gaussian mean mass in the 2 b category. Different widths, from 0% up to 15% of the signal mass, are considered. Gaussian-shape signals with 0% widths correspond to signal widths smaller than the experimental resolution. For a Gaussian-shaped signal with a relative width of 15%, the limits are truncated at high mass when the broad signal starts to overlap the upper end of the mass spectrum.
The expected 95% CL upper limits on the cross-section times acceptance times b-tagging efficiency times branching ratio as a function of the DM mediator Z' mass for the current and previous iterations of the analysis. The upper limit of the previous result was obtained with the Bayesian method and is also shown scaled to the 139 fb$^{-1}$ integrated luminosity of the current result to illustrate the effect of the analysis improvements.
Acceptance for the QBH, Q* and W' benchmark signal models in the inclusive category, as a function of the simulated mass.
Acceptance for the DM Z' benchmark signal model for various $g_q$ coupling parameters in the inclusive category, as a function of the simulated mass.
Acceptance for the W* benchmark signal model in the inclusive category, as a function of the simulated mass.
Acceptance times b-tagging efficiency for the b* benchmark signal model in the 1b category, as a function of the simulated mass.
Acceptance times b-tagging efficiency for the DM Z' with $g_q$=0.25, SSM Z' and graviton with $k/\overline{M}_{PL}=0.2$ benchmark signal models in the 2b category, as a function of the simulated mass.
The expected 95% CL upper limits on the cross-section times branching ratio as a function of the DM mediator Z' mass for the current and previous iterations of the analysis. The upper limit of the previous result was obtained with the Bayesian method and is also shown scaled to the 139 fb$^{-1}$ integrated luminosity of the current result to illustrate the effect of the analysis improvements. The current b-tagging requirement is tighter than the previous one for high-$p_T$ jets, resulting in a data sample with limited size for mass above 4 TeV. The background rejection, instead, has improved significantly across the entire mass spectrum inspected by the analysis.
The results of a search for electroweakino pair production $pp \rightarrow \tilde\chi^\pm_1 \tilde\chi^0_2$ in which the chargino ($\tilde\chi^\pm_1$) decays into a $W$ boson and the lightest neutralino ($\tilde\chi^0_1$), while the heavier neutralino ($\tilde\chi^0_2$) decays into the Standard Model 125 GeV Higgs boson and a second $\tilde\chi^0_1$ are presented. The signal selection requires a pair of $b$-tagged jets consistent with those from a Higgs boson decay, and either an electron or a muon from the $W$ boson decay, together with missing transverse momentum from the corresponding neutrino and the stable neutralinos. The analysis is based on data corresponding to 139 $\mathrm{fb}^{-1}$ of $\sqrt{s}=13$ TeV $pp$ collisions provided by the Large Hadron Collider and recorded by the ATLAS detector. No statistically significant evidence of an excess of events above the Standard Model expectation is found. Limits are set on the direct production of the electroweakinos in simplified models, assuming pure wino cross-sections. Masses of $\tilde{\chi}^{\pm}_{1}/\tilde{\chi}^{0}_{2}$ up to 740 GeV are excluded at 95% confidence level for a massless $\tilde{\chi}^{0}_{1}$.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onLM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onLM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onLM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onLM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onMM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onMM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onMM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onMM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onHM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onHM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onHM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-onHM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offLM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offLM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offLM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offLM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offMM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offMM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offMM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offMM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offHM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offHM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offHM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution is shown in the validation region VR-offHM after all the selection requirements are applied other than the $m_{CT}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. The red line with arrow indicates the $m_{CT}$ cut used in SR selection. The first and the last bin include the underflow and overflow events (where present), respectively.
The post-fit $m_{CT}$ distribution for SR-HM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-HM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-HM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-HM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-MM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-MM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-MM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-MM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-LM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-LM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-LM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{CT}$ distribution for SR-LM. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-HM after all the selection requirements are applied other than the $m_{bb}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection.The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-HM after all the selection requirements are applied other than the $m_{bb}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection.The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-HM after all the selection requirements are applied other than the $m_{bb}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection.The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-HM after all the selection requirements are applied other than the $m_{bb}$ cut. The stacked histograms show the expected SM backgrounds. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection.The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-MM after all the selection requirements are applied other than the $m_{bb}$ cut. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection. The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-MM after all the selection requirements are applied other than the $m_{bb}$ cut. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection. The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-MM after all the selection requirements are applied other than the $m_{bb}$ cut. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection. The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-MM after all the selection requirements are applied other than the $m_{bb}$ cut. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection. The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-LM after all the selection requirements are applied other than the $m_{bb}$ cut. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection. The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-LM after all the selection requirements are applied other than the $m_{bb}$ cut. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection. The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-LM after all the selection requirements are applied other than the $m_{bb}$ cut. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection. The overflow events, where present, are included in the last bin.
The post-fit $m_{bb}$ distribution is shown in the signal region SR-LM after all the selection requirements are applied other than the $m_{bb}$ cut. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distribution of the SUSY reference points are also shown as dashed lines. The red line with arrow indicates the $m_{bb}$ cut used in SR selection. The overflow events, where present, are included in the last bin.
The observed exclusion for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.
The observed exclusion for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.
The observed exclusion for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.
The observed exclusion for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.
The observed exclusion up limit for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. The red dotted lines indicate the $\pm 1 \sigma$ on the observed exclusion limit due to the theoretical uncertainties in the signal cross-section.
The observed exclusion up limit for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. The red dotted lines indicate the $\pm 1 \sigma$ on the observed exclusion limit due to the theoretical uncertainties in the signal cross-section.
The observed exclusion up limit for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. The red dotted lines indicate the $\pm 1 \sigma$ on the observed exclusion limit due to the theoretical uncertainties in the signal cross-section.
The observed exclusion up limit for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. The red dotted lines indicate the $\pm 1 \sigma$ on the observed exclusion limit due to the theoretical uncertainties in the signal cross-section.
The observed exclusion down limit for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. The red dotted lines indicate the $\pm 1 \sigma$ on the observed exclusion limit due to the theoretical uncertainties in the signal cross-section.
The observed exclusion down limit for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. The red dotted lines indicate the $\pm 1 \sigma$ on the observed exclusion limit due to the theoretical uncertainties in the signal cross-section.
The observed exclusion down limit for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. The red dotted lines indicate the $\pm 1 \sigma$ on the observed exclusion limit due to the theoretical uncertainties in the signal cross-section.
The observed exclusion down limit for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. The red dotted lines indicate the $\pm 1 \sigma$ on the observed exclusion limit due to the theoretical uncertainties in the signal cross-section.
The expected exclusion for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.
The expected exclusion for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.
The expected exclusion for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.
The expected exclusion for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. Experimental and theoretical systematic uncertainties are applied to background and signal samples and illustrated by the yellow band and the red dotted contour lines, respectively. The red dotted lines indicate the $\pm$ 1 standard-deviation variation on the observed exclusion limit due to theoretical uncertainties in the signal cross-section.
Upper limits on the cross sections for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Upper limits on the cross sections for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Upper limits on the cross sections for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Upper limits on the cross sections for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-LM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-MM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. 1lb\bar{b}$ production
Signal acceptance in SR-HM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. 1lb\bar{b}$ production
Signal acceptance in SR-HM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. 1lb\bar{b}$ production
Signal acceptance in SR-HM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production. 1lb\bar{b}$ production
Signal acceptance in SR-HM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal acceptance in SR-HM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-LM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-MM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM low $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM med. $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Signal efficiency in SR-HM high $m_{CT}$ for simplified models with $\tilde\chi^\pm_1 \tilde\chi^0_2 \rightarrow Wh\tilde\chi^0_1\tilde\chi^0_1, W \rightarrow l\nu, h \rightarrow b\bar{b}$ production.
Event selection cutflow for a representative signal sample for the SR-LM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-LM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-MM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM low $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM med. $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the SR-HM high $m_{CT}$. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-LM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-LM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-LM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-LM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-MM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-MM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-MM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-MM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-HM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-HM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-HM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
Event selection cutflow for a representative signal sample for the discovery SR-HM. The masses of next-lightest-neutralinos and LSPs are reported. While the first row of the table reports the total raw MC events produced, all subsequent rows show weighted events. Only statistical uncertainties are shown. Samples are produced with generator filters which selects $h\rightarrow b\bar{b}$ and $W\rightarrow\ell\nu$ decays.
A search for supersymmetric partners of gluons and quarks is presented, involving signatures with jets and either two isolated leptons (electrons or muons) with the same electric charge, or at least three isolated leptons. A data sample of proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded with the ATLAS detector at the Large Hadron Collider between 2015 and 2018, corresponding to a total integrated luminosity of 139 fb$^{-1}$, is used for the search. No significant excess over the Standard Model expectation is observed. The results are interpreted in simplified supersymmetric models featuring both R-parity conservation and R-parity violation, raising the exclusion limits beyond those of previous ATLAS searches to 1600 GeV for gluino masses and 750 GeV for bottom and top squark masses in these scenarios.
Observed 95% CL exclusion contours in signal region Rpc2L0b on the gluino and lightest neutralino masses in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g \to q \bar{q}^{'} \tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm \to W^\pm \tilde{\chi}_2^0$ and $ \tilde{\chi}_2^0 \to Z \tilde{\chi}_1^0$.
Observed 95% CL exclusion contours in signal region Rpc2L0b on the gluino and lightest neutralino masses in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g \to q \bar{q}^{'} \tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm \to W^\pm \tilde{\chi}_2^0$ and $ \tilde{\chi}_2^0 \to Z \tilde{\chi}_1^0$.
Observed 95% CL exclusion contours in signal region Rpc2L0b on the gluino and lightest neutralino masses in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g \to q \bar{q}^{'} \tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm \to W^\pm \tilde{\chi}_2^0$ and $ \tilde{\chi}_2^0 \to Z \tilde{\chi}_1^0$.
Observed 95% CL exclusion contours in signal region Rpc2L0b on the gluino and lightest neutralino masses in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g \to q \bar{q}^{'} \tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm \to W^\pm \tilde{\chi}_2^0$ and $ \tilde{\chi}_2^0 \to Z \tilde{\chi}_1^0$.
Expected 95% CL exclusion contours in signal region Rpc2L0b on the gluino and lightest neutralino masses in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Expected 95% CL exclusion contours in signal region Rpc2L0b on the gluino and lightest neutralino masses in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Expected 95% CL exclusion contours in signal region Rpc2L0b on the gluino and lightest neutralino masses in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Expected 95% CL exclusion contours in signal region Rpc2L0b on the gluino and lightest neutralino masses in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Observed 95% CL exclusion contours in signal region Rpv2L on the gluino and lightest top squark masses in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Observed 95% CL exclusion contours in signal region Rpv2L on the gluino and lightest top squark masses in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Observed 95% CL exclusion contours in signal region Rpv2L on the gluino and lightest top squark masses in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Observed 95% CL exclusion contours in signal region Rpv2L on the gluino and lightest top squark masses in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Expected 95% CL exclusion contours in signal region Rpv2L on the gluino and lightest top squark masses in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Expected 95% CL exclusion contours in signal region Rpv2L on the gluino and lightest top squark masses in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Expected 95% CL exclusion contours in signal region Rpv2L on the gluino and lightest top squark masses in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Expected 95% CL exclusion contours in signal region Rpv2L on the gluino and lightest top squark masses in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Observed 95% CL exclusion contours in the best combination of signal regions of Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Observed 95% CL exclusion contours in the best combination of signal regions of Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Observed 95% CL exclusion contours in the best combination of signal regions of Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Observed 95% CL exclusion contours in the best combination of signal regions of Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Expected 95% CL exclusion contours in the best combination of signal regions of Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Expected 95% CL exclusion contours in the best combination of signal regions of Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Expected 95% CL exclusion contours in the best combination of signal regions of Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Expected 95% CL exclusion contours in the best combination of signal regions of Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L0b, in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde g)$ = 1600 GeV, $m(\tilde \chi_1^\pm)$ = 1200 GeV, $m(\tilde \chi_2^0)$ = 1000 GeV and $m(\tilde \chi_1^0)$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L0b, in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde g)$ = 1600 GeV, $m(\tilde \chi_1^\pm)$ = 1200 GeV, $m(\tilde \chi_2^0)$ = 1000 GeV and $m(\tilde \chi_1^0)$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L0b, in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde g)$ = 1600 GeV, $m(\tilde \chi_1^\pm)$ = 1200 GeV, $m(\tilde \chi_2^0)$ = 1000 GeV and $m(\tilde \chi_1^0)$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L0b, in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde g)$ = 1600 GeV, $m(\tilde \chi_1^\pm)$ = 1200 GeV, $m(\tilde \chi_2^0)$ = 1000 GeV and $m(\tilde \chi_1^0)$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L1b, in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde{b}^{}_1)$ = 850 GeV, $m(\tilde \chi_1^\pm)$ = 500 GeV and $m(\tilde \chi_1^0)$ = 400 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L1b, in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde{b}^{}_1)$ = 850 GeV, $m(\tilde \chi_1^\pm)$ = 500 GeV and $m(\tilde \chi_1^0)$ = 400 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L1b, in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde{b}^{}_1)$ = 850 GeV, $m(\tilde \chi_1^\pm)$ = 500 GeV and $m(\tilde \chi_1^0)$ = 400 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L1b, in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde{b}^{}_1)$ = 850 GeV, $m(\tilde \chi_1^\pm)$ = 500 GeV and $m(\tilde \chi_1^0)$ = 400 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L2b, in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde{b}^{}_1)$ = 850 GeV, $m(\tilde \chi_1^\pm)$ = 500 GeV and $m(\tilde \chi_1^0)$ = 400 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L2b, in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde{b}^{}_1)$ = 900 GeV, $m(\tilde \chi_1^\pm)$ = 150 GeV and $m(\tilde \chi_1^0)$ = 50 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L2b, in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde{b}^{}_1)$ = 900 GeV, $m(\tilde \chi_1^\pm)$ = 150 GeV and $m(\tilde \chi_1^0)$ = 50 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc2L2b, in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$. The masses of the superpartners involved in the process are set to $m(\tilde{b}^{}_1)$ = 900 GeV, $m(\tilde \chi_1^\pm)$ = 150 GeV and $m(\tilde \chi_1^0)$ = 50 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc3LSS1b, in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate. The masses of the superpartners involved in the process are set to $m(\tilde{t}^{}_1)$ = 800 GeV, $m(\tilde \chi_2^0)$ = 625 GeV, $m(\tilde \chi_1^\pm)\approx m(\tilde \chi_1^0)$ = 525 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc3LSS1b, in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate. The masses of the superpartners involved in the process are set to $m(\tilde{t}^{}_1)$ = 800 GeV, $m(\tilde \chi_2^0)$ = 625 GeV, $m(\tilde \chi_1^\pm)\approx m(\tilde \chi_1^0)$ = 525 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc3LSS1b, in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate. The masses of the superpartners involved in the process are set to $m(\tilde{t}^{}_1)$ = 800 GeV, $m(\tilde \chi_2^0)$ = 625 GeV, $m(\tilde \chi_1^\pm)\approx m(\tilde \chi_1^0)$ = 525 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpc3LSS1b, in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate. The masses of the superpartners involved in the process are set to $m(\tilde{t}^{}_1)$ = 800 GeV, $m(\tilde \chi_2^0)$ = 625 GeV, $m(\tilde \chi_1^\pm)\approx m(\tilde \chi_1^0)$ = 525 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpv2L, in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$. The masses of the superpartners involved in the process are set to $m(\tilde g)$ = 1600 GeV, $m(\tilde{t}^{}_{1})$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpv2L, in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$. The masses of the superpartners involved in the process are set to $m(\tilde g)$ = 1600 GeV, $m(\tilde{t}^{}_{1})$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpv2L, in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$. The masses of the superpartners involved in the process are set to $m(\tilde g)$ = 1600 GeV, $m(\tilde{t}^{}_{1})$ = 800 GeV. Only statistical uncertainties are shown.
Number of signal events expected for 139 fb$^{-1}$ at different stages of the event selection for the signal region Rpv2L, in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$. The masses of the superpartners involved in the process are set to $m(\tilde g)$ = 1600 GeV, $m(\tilde{t}^{}_{1})$ = 800 GeV. Only statistical uncertainties are shown.
Signal acceptance for Rpc2L0b signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L0b signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L0b signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L0b signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L1b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L1b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L1b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L1b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L2b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L2b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L2b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal acceptance for Rpc2L2b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal acceptance for Rpv2L signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Signal acceptance for Rpv2L signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Signal acceptance for Rpv2L signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Signal acceptance for Rpv2L signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Signal acceptance for Rpc3LSS1b signal region with sensitivity to $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Signal acceptance for Rpc3LSS1b signal region with sensitivity to $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Signal acceptance for Rpc3LSS1b signal region with sensitivity to $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Signal acceptance for Rpc3LSS1b signal region with sensitivity to $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Signal efficiency for Rpc2L0b signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L0b signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L0b signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L0b signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L1b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L1b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L1b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L1b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L2b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L2b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L2b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal efficiency for Rpc2L2b signal region with sensitivity to $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Signal efficiency for Rpv2L signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Signal efficiency for Rpv2L signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Signal efficiency for Rpv2L signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Signal efficiency for Rpv2L signal region with sensitivity to $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Signal efficiency for Rpc3LSS1b signal region with sensitivity to $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Signal efficiency for Rpc3LSS1b signal region with sensitivity to $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Signal efficiency for Rpc3LSS1b signal region with sensitivity to $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Signal efficiency for Rpc3LSS1b signal region with sensitivity to $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Observed 95% CL upper limit on $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Observed 95% CL upper limit on $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Observed 95% CL upper limit on $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Observed 95% CL upper limit on $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into the lightest neutralino via a two-steps cascade, $\tilde g\to q\bar{q}^{'}\tilde{\chi}_1^\pm$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_2^0$ and $\tilde{\chi}_2^0\to Z\tilde{\chi}_1^0$.
Observed 95% CL upper limit on $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Observed 95% CL upper limit on $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Observed 95% CL upper limit on $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Observed 95% CL upper limit on $pp\to \tilde g\tilde g$ production cross-sections in a SUSY scenario where gluinos are produced in pairs and decay into a top quark and an top squark, which in turn decays via non-zero baryon-number-violating RPV couplings $\lambda^{''}_{313}$, $\tilde g\to t\tilde{t}_1$ followed by $\tilde{t}_1\to b d$.
Observed 95% CL upper limit on $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Observed 95% CL upper limit on $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Observed 95% CL upper limit on $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Observed 95% CL upper limit on $pp\to \tilde{b}^{}_1\tilde{b}^{*}_1$ production cross-sections in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Observed 95% CL upper limit on $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Observed 95% CL upper limit on $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Observed 95% CL upper limit on $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Observed 95% CL upper limit on $pp\to \tilde{t}^{}_\mathrm{1}\tilde{t}^{*}_\mathrm{1}$ production cross-sections in a SUSY scenario where pairs of top-antitop squarks are produced and decay into the lightest neutralino via a two-steps cascade, $\tilde t^{}_{1}\to t\tilde{\chi}_2^0$ followed by $\tilde{\chi}_2^0\to \tilde{\chi}_1^\pm W^\mp$ and $\tilde{\chi}_1^\pm\to f\bar{f^{'}}\tilde{\chi}_1^0$. The lightest chargino and the lightest neutralino are assumed to be nearly mass-degenerate.
Best observed 95% CL exclusion contours selected from Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Best observed 95% CL exclusion contours selected from Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Best observed 95% CL exclusion contours selected from Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
Best observed 95% CL exclusion contours selected from Rpc2L1b and Rpc2L2b on the lightest bottom squark and lightest neutralino masses in a SUSY scenario where pairs of bottom-antibottom squarks are produced and decay into the lightest neutralino via a chargino, $\tilde b^{}_{1}\to t\tilde{\chi}_1^-$ followed by $\tilde{\chi}_1^\pm\to W^\pm\tilde{\chi}_1^0$.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}}$ of observed data and expected background towards Rpc2L0b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}}$ of observed data and expected background towards Rpc2L0b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}}$ of observed data and expected background towards Rpc2L0b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}}$ of observed data and expected background towards Rpc2L0b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}} / m_{\mathrm{eff}}$ of observed data and expected background towards Rpc2L1b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}} / m_{\mathrm{eff}}$ of observed data and expected background towards Rpc2L1b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}} / m_{\mathrm{eff}}$ of observed data and expected background towards Rpc2L1b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}} / m_{\mathrm{eff}}$ of observed data and expected background towards Rpc2L1b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}}$ of observed data and expected background towards Rpc2L2b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}}$ of observed data and expected background towards Rpc2L2b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}}$ of observed data and expected background towards Rpc2L2b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $E_{\mathrm{T}}^{\mathrm{miss}}$ of observed data and expected background towards Rpc2L2b from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $m_{\mathrm{eff}}$ of observed data and expected background towards Rpv2L from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $m_{\mathrm{eff}}$ of observed data and expected background towards Rpv2L from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $m_{\mathrm{eff}}$ of observed data and expected background towards Rpv2L from publication's Figure 5 . The last bin is inclusive.
N-1 distributions for $m_{\mathrm{eff}}$ of observed data and expected background towards Rpv2L from publication's Figure 5 . The last bin is inclusive.
The elliptic flow of muons from the decay of charm and bottom hadrons is measured in $pp$ collisions at $\sqrt{s}=13$ TeV using a data sample with an integrated luminosity of 150 pb$^{-1}$ recorded by the ATLAS detector at the LHC. The muons from heavy-flavor decay are separated from light-hadron decay muons using momentum imbalance between the tracking and muon spectrometers. The heavy-flavor decay muons are further separated into those from charm decay and those from bottom decay using the distance-of-closest-approach to the collision vertex. The measurement is performed for muons in the transverse momentum range 4-7 GeV and pseudorapidity range $|\eta|<2.4$. A significant non-zero elliptic anisotropy coefficient $v_{2}$ is observed for muons from charm decays, while the $v_{2}$ value for muons from bottom decays is consistent with zero within uncertainties.
Summary of results for inclusive muon v2 as a function of multiplicity. Uncertainties are statistical and systematic, respectively.
Summary of results for inclusive muon v2 as a function of pT. Uncertainties are statistical and systematic, respectively.
Summary of results for charm and bottom muon v2 as a function of multiplicity. Uncertainties are statistical and systematic, respectively.
Summary of results for charm and bottom muon v2 as a function of pT. Uncertainties are statistical and systematic, respectively.
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