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A search for the pair production of heavy leptons as predicted by the type-III seesaw mechanism is presented. The search uses proton-proton collision data at a centre-of-mass energy of 13 TeV, corresponding to 139 fb$^{-1}$ of integrated luminosity recorded by the ATLAS detector during Run 2 of the Large Hadron Collider. The analysis focuses on final states with three or four electrons or muons from the possible decays of new heavy leptons via intermediate electroweak bosons. No significant deviations above the Standard Model expectation are observed; upper and lower limits on the heavy lepton production cross-section and masses are derived respectively. These results are then combined for the first time with the ones already published by ATLAS using the channel with two leptons in the final state. The observed lower limit on the mass of the type-III seesaw heavy leptons combining two, three and four lepton channels together is 910 GeV at the 95% confidence level.
Expected background yields and observed data after the background-only fit in the SRs.
Distribution of $m_{\mathrm{T},3l}$ in the ZL SR after the background-only fit. The uncertainty on the expected number of background events includes all statistical and systematic post-fit uncertainties with the correlations between various background sources taken into account.
Distribution of $m_{\mathrm{T},3l}$ in the ZL Veto SR after the background-only fit. The uncertainty on the expected number of background events includes all statistical and systematic post-fit uncertainties with the correlations between various background sources taken into account.
Distribution of $m_{\mathrm{T},3l}$ in the JNLow SR after the background-only fit. The uncertainty on the expected number of background events includes all statistical and systematic post-fit uncertainties with the correlations between various background sources taken into account.
Distribution of $H_\text{T} + E_\text{T}^\text{miss}$ in the Q0 SR after the background-only fit. The uncertainty on the expected number of background events includes all statistical and systematic post-fit uncertainties with the correlations between various background sources taken into account.
Distribution of $H_\text{T} + E_\text{T}^\text{miss}$ in the Q2 SR after the background-only fit. The uncertainty on the expected number of background events includes all statistical and systematic post-fit uncertainties with the correlations between various background sources taken into account.
Distribution of $m_{\mathrm{T},3l}$ in the ZL-CR after the background-only fit.
Distribution of $m_{\mathrm{T},3l}$ in the Fake-CR after the background-only fit.
Distribution of $H_\text{T} + E_\text{T}^\text{miss}$ in the Q0 DB-CR after the background-only fit.
Distribution of $H_\text{T} + E_\text{T}^\text{miss}$ in the Q0 RT-CR after the background-only fit.
Expected and observed exclusion limit for the combination of the two- (from Ref. EXOT-2018-33), three- and four-lepton channels, for the type-III seesaw process with the corresponding one- and two-standard-deviation uncertainty bands, showing the 95% CL upper limit on the cross-section.
Expected and observed 95% ( $CL_s$ ) exclusion limits in the three lepton channel for the type-III seesaw process with the corresponding one- and two-standard-deviation bands, showing the 95% CL upper limit on the cross-section.
Expected and observed 95% ( $CL_s$ ) exclusion limits in the four lepton channel for the type-III seesaw process with the corresponding one- and two-standard-deviation bands, showing the 95% CL upper limit on the cross-section.
Cross-sections of signal Monte Carlo samples for each mass point considered in this analysis. Leading order cross-sections ( $\sigma_{LO}$ ) are computed by the generator and then rescaled to next-to-leading cross-sections ( $\sigma_{NLO+NLL}$ ), with their corresponding uncertainties, using information taken from Refs. C1C1 and N2C1.
Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the ZL SR. Preselection represents events with at least three leptons.
Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the ZLVeto SR. Preselection represents events with at least three leptons.
Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the JNLow SR. Preselection represents events with at least three leptons.
Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the Q0 SR. Preselection represents events with at least three leptons.
Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the Q2 SR. Preselection represents events with at least three leptons.
Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the ZL CR. Preselection represents events with at least three leptons.
Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the Q0-DB CR. Preselection represents events with at least three leptons.
Expected signal and background yields after each of the analysis selection cuts for the 800 GeV mass hypothesis in the Q0-RT CR. Preselection represents events with at least three leptons.
Expected background yields and observed data after the background-only fit, in the three-lepton CRs and VRs.
Expected background yields and observed data after the background-only fit, in the four-lepton CRs and VRs.
Expected and observed 95% ( $CL_s$ ) exclusion limits in the three and four lepton channels for the type-III seesaw process with the corresponding one- and two-standard-deviation bands, showing the 95% CL upper limit on the cross-section.
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 resonant Higgs boson pair production in the $b\bar{b}b\bar{b}$ final state is presented. The analysis uses 126-139 fb$^{-1}$ of $pp$ collision data at $\sqrt{s}$ = 13 TeV collected with the ATLAS detector at the Large Hadron Collider. The analysis is divided into two channels, targeting Higgs boson decays which are reconstructed as pairs of small-radius jets or as individual large-radius jets. Spin-0 and spin-2 benchmark signal models are considered, both of which correspond to resonant $HH$ production via gluon$-$gluon fusion. The data are consistent with Standard Model predictions. Upper limits are set on the production cross-section times branching ratio to Higgs boson pairs of a new resonance in the mass range from 251 GeV to 5 TeV.
Cumulative acceptance times efficiency as a function of resonance mass for each event selection step in the resolved channel for the spin-0 signal models. The local maximum at 251 GeV is a consequence of the near-threshold kinematics.
Cumulative acceptance times efficiency as a function of resonance mass for each event selection step in the resolved channel for the spin-2 signal models. The local maximum at 251 GeV is a consequence of the near-threshold kinematics.
Corrected $m(HH)$ distribution in the resolved $4b$ validation region (dots), compared with the reweighted distribution in $2b$ validation region (teal histogram). The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The final bin includes overflow. The background uncertainty (gray band) is computed by adding all individual components in quadrature. The bottom panel shows the difference between the $4b$ and reweighted $2b$ distributions, relative to the $2b$ distribution.
Corrected $m(HH)$ distribution in the resolved $4b$ signal region (dots), after the fit under the background-only hypothesis. The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The background model (teal histogram) is shown with its total post-fit uncertainty (gray band). The final bin includes overflow. Representative spin-0 signal hypotheses (dashed, dotted, and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. The bottom panel shows the difference between the $4b$ distribution and the background model, relative to the background model. No significant excess of data relative to the SM background is observed.
Corrected $m(HH)$ distribution in the resolved $4b$ signal region (dots), after the fit under the background-only hypothesis. The error bars on the $4b$ points represent the Poisson uncertainties corresponding to their event yields. The background model (teal histogram) is shown with its total post-fit uncertainty (gray band). The final bin includes overflow. Representative spin-2 signal hypotheses (dashed, dotted, and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. The bottom panel shows the difference between the $4b$ distribution and the background model, relative to the background model. No significant excess of data relative to the SM background is observed.
Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The steps up to the $b$-tag categorization are shown for the spin-0.
Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The steps up to the $b$-tag categorization are shown for the spin-2.
Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The efficiencies of the three b-tag categories are shown for the spin-0 scenario; this efficiency is obtained after the other selection steps including the SR definition. The signal efficiency in the 4b region has a maximum around 1.5 TeV. Above that value the track-jets start to merge together, and for the highest resonance masses the 2b category becomes the most efficient.
Cumulative signal acceptance times efficiency as a function of the resonance mass for various selection steps in the boosted channel. The efficiencies of the three b-tag categories are shown for the spin-2 scenario; this efficiency is obtained after the other selection steps including the SR definition. The signal efficiency in the 4b region has a maximum around 1.5 TeV. Above that value the track-jets start to merge together, and for the highest resonance masses the 2b category becomes the most efficient.
Comparison of the background model (stacked histograms) with data (dots) in the $2b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.
Comparison of the background model (stacked histograms) with data (dots) in the $3b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.
Comparison of the background model (stacked histograms) with data (dots) in the $4b$ validation region. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background uncertainty (gray band) is computed by adding all individual components in quadrature and is not allowed to extend below zero.
The $m(HH)$ distributions in the boosted $2b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.
The $m(HH)$ distributions in the boosted $2b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.
The $m(HH)$ distributions in the boosted $3b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.
The $m(HH)$ distributions in the boosted $3b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.
The $m(HH)$ distributions in the boosted $4b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-0 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.
The $m(HH)$ distributions in the boosted $4b$ signal regions (dots), after the fit under the background-only hypothesis. The error bars on the data points represent the Poisson uncertainties corresponding to their event yields. The background model (stacked histogram) is shown with its total post-fit uncertainty (gray band). The uncertainty bands are defined using an ensemble of curves constructed by sampling a multivariate Gaussian probability density function built from the covariance matrix of the fit. Representative spin-2 signal hypotheses (dashed and dashed-dotted lines) are overlaid, normalized to the overall expected limits on their cross-sections. No significant excess of data relative to the SM background is observed.
Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of resonant $HH$ production in the spin-0 signal models. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits (colored bands) are shown. Expected limits using each of the resolved and boosted channels individually (dashed colored lines) are shown. The nominal $H\rightarrow b\bar{b}$ branching ratio is taken as 0.582.
Expected (dashed black lines) and observed (solid black lines) 95% CL upper limits on the cross-section of resonant $HH$ production in the spin-2 signal models. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits (colored bands) are shown. Expected limits using each of the resolved and boosted channels individually (dashed colored lines) are shown. The theoretical prediction for the bulk RS model with $k/\bar{M}_{\text{Pl}} = 1$ (solid red line) is shown; the decrease below 350 GeV is due to a sharp reduction in the $G^{*}_{\text{KK}} \rightarrow HH$ branching ratio. The nominal $H\rightarrow b\bar{b}$ branching ratio is taken as 0.582.
A direct search for Higgs bosons produced via vector-boson fusion and subsequently decaying into invisible particles is reported. The analysis uses 139 $\text{fb}^{-1}$ of $pp$ collision data at a centre-of-mass energy of $\sqrt{s}$=13 $\text{TeV}$ recorded by the ATLAS detector at the LHC. The observed numbers of events are found to be in agreement with the background expectation from Standard Model processes. For a scalar Higgs boson with a mass of 125 $\text{GeV}$ and a Standard Model production cross section, an observed upper limit of $0.145$ is placed on the branching fraction of its decay into invisible particles at 95% confidence level, with an expected limit of $0.103$. These results are interpreted in the context of models where the Higgs boson acts as a portal to dark matter, and limits are set on the scattering cross section of weakly interacting massive particles and nucleons. Invisible decays of additional scalar bosons with masses from 50 $\text{GeV}$ to 2 $\text{TeV}$ are also studied, and the derived upper limits on the cross section times branching fraction decrease with increasing mass from 1.0 $\text{pb}$ for a scalar boson mass of 50 $\text{GeV}$ to 0.1 $\text{pb}$ at a mass of 2 $\text{TeV}$.
Yields after each selection criterion for a signal sample of an invisibly decaying Higgs boson produced in VBF and ggF for 139 $fb^{-1}$ of data. The lines 'Timing of j1/j2' are referring to requirements that are part of the jet cleaning, and which ensure that the timing of the two leading jets is compatible with the bunch crossing. The last sixteen rows show the yield in each SR bin and the efficiency with respect to the inclusive signal region.
An inclusive search for nonresonant signatures of beyond the standard model (SM) phenomena in events with three or more charged leptons, including hadronically decaying $\tau$ leptons, is presented. The analysis is based on a data sample corresponding to an integrated luminosity of 138 fb$^{-1}$ of proton-proton collisions at $\sqrt{s} =$ 13 TeV, collected by the CMS experiment at the LHC in 2016-2018. Events are categorized based on the lepton and b-tagged jet multiplicities and various kinematic variables. Three scenarios of physics beyond the SM are probed, and signal-specific boosted decision trees are used for enhancing sensitivity. No significant deviations from the background expectations are observed. Lower limits are set at 95% confidence level on the mass of type-III seesaw heavy fermions in the range 845-1065 GeV for various decay branching fraction combinations to SM leptons. Doublet and singlet vector-like $\tau$ lepton extensions of the SM are excluded for masses below 1045 GeV and in the mass range 125-150 GeV, respectively. Scalar leptoquarks decaying exclusively to a top quark and a lepton are excluded below 1.12-1.42 TeV, depending on the lepton flavor. For the type-III seesaw as well as the vector-like doublet model, these constraints are the most stringent to date. For the vector-like singlet model, these are the first constraints from the LHC experiments. Detailed results are also presented to facilitate alternative theoretical interpretations.
The minimum lepton $\mathrm{p_{T}}$ (GeV) distribution in 3L MisID CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The $\mathrm{S_{T}}$ (GeV) distribution in 3L WZ CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The $\mathrm{DR_{min}}$ distribution in 3L Z$\mathrm{\gamma}$ CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The distribution of $\mathrm{p_{T}^{miss}}$ (GeV) in 2L1T MisID CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The distribution of $\mathrm{M_{T}}$ (GeV) in 3L OnZ CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The distribution of $\mathrm{H_{T}}$ (GeV) in 3L ttZ CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
Distribution of BDT score from the SS-M ($\mathrm{B_{e}=B_{\mu}=B_{\tau}}$) BDT for the 3L+2L1T CR events for the combined 2016-2018 data set. The 3L+2L1T CR consists of the 3L OnZ, 3L Z$\mathrm{\gamma}$, and 2L1T MisID CRs. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The distribution of visible diboson $\mathrm{p_{T}}$ (GeV) in 4L ZZ CR events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
Distribution of BDT score from the SS-M ($\mathrm{B_{e}=B_{\mu}=B_{\tau}}$) BDT for the 4L ZZ CR events for the combined 2016-2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The distribution of $\mathrm{L_{T}}$ in all seven multilepton channels for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton of $\mathrm{m_{\tau'}}$ = 1 TeV in the doublet scenario, before the fit, is also overlaid.
The distribution of $\mathrm{p_{T}^{miss}}$ in all seven multilepton channels for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermion of $\mathrm{m_{\Sigma}}$ = 1 TeV in the flavor-democratic scenario, before the fit, is also overlaid.
The distribution of $\mathrm{H_{T}}$ in all seven multilepton channels for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. For illustration, an example signal hypothesis for the production of the scalar leptoquark of $mathrm{m_{S}}$ = 1 TeV coupled to a top quark and a $\tau$ lepton, before the fit, is also overlaid.
The distribution of $\mathrm{M_{OSSF}}$ in channels with at least one light lepton pair (4L, 3L1T, 3L, 2L2T, and 2L1T) for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermion of $\mathrm{m_{\Sigma}}$ = 1 TeV in the flavor-democratic scenario, before the fit, is also overlaid.
The $\mathrm{N_{b}}$ distribution in 4L, 3L1T, 3L, 2L2T, 2L1T, 1L3T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The invariant mass distribution of the opposite-sign same-flavor ($\mathrm{M_{OSSF}}$) tau lepton pair distribution in 2L2T, 1L3T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The $\mathrm{M_{T}^{12}}$ distribution in 4L, 3L1T, 3L, 2L2T, 2L1T, 1L3T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction.
The $\mathrm{N_{b}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The $\mathrm{L_{T}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The $\mathrm{p_{T}^{miss}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The $\mathrm{H_{T}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The $\mathrm{M_{OSSF}}$ distribution in 3L, and 2L1T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The invariant mass distribution of the opposite-sign different-flavor ($\mathrm{M_{OSDF}}$) light lepton pair distribution in 3L, and 2L1T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The invariant mass distribution of the opposite-sign same-flavor ($\mathrm{M_{OSSF}}$) tau lepton pair distribution in 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The invariant mass distribution of the opposite-sign different-flavor ($\mathrm{M_{OSDF}}$) light lepton and tau lepton pair distribution in 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The $\mathrm{M_{T}^{1}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The $\mathrm{M_{T}^{12}}$ distribution in 3L, 2L1T, and 1L2T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The model independent fundamental table categories for the combined 2016-2018 data set, as defined in Table 1. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The $\mathrm{N_{b}}$ distribution in 4L, 3L1T, 2L2T, and 1L3T events for the combined 2016-2018 data set. The rightmost bin contains the overflow events. The gray band represents the sum of statistical and systematic uncertainties on the SM background predictions.
The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 3L channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 2L1T and 1L2T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 3L channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 2L1T and 1L2T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 4L, 3L1T, 2L2T, and 1L3T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table in 4L, 3L1T, 2L2T, and 1L3T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 3L channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 2L1T and 1L2T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 3L channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 2L1T and 1L2T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 4L, 3L1T, 2L2T, and 1L3T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the fundamental $\mathrm{S_{T}}$ table in 4L, 3L1T, 2L2T, and 1L3T channels for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 1B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 1B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 1L2T channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. An example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid. For this category, the signal yield is negligible and is not visible in the figure.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 1B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L 2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 1B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 2L1T 2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 1L2T channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. An example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid. For this category, the signal yield is negligible and is not visible in the figure.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 4L 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 4L 1B/2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L1T, 2L2T, and 1L3T channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 4L 0B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 4L 1B/2B channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The SR distributions of the advanced $\mathrm{S_{T}}$ table in 3L1T, 2L2T, and 1L3T channel for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The VLL-L BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.
The VLL-L BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.
The VLL-L BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.
The VLL-M BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.
The VLL-M BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.
The VLL-M BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.
The VLL-H BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.
The VLL-H BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.
The VLL-H BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.
The VLL-L BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.
The VLL-L BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.
The VLL-L BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 200 GeV, before the fit, is also overlaid.
The VLL-M BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.
The VLL-M BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.
The VLL-M BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 400 GeV, before the fit, is also overlaid.
The VLL-H BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.
The VLL-H BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.
The VLL-H BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 900 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 100 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 300 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 550 GeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.
The SS-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the scenario with mixing exclusively to $\tau$ lepton for $\mathrm{m_{\Sigma}}$ = 850 GeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 3-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{e}+B_{\mu}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and an electron for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-VL $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 200 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-L $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 400 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-M $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 700 GeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2016 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2017 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.
The LQ-H $\mathrm{B_{\tau}=1}$ BDT regions for the 4-lepton channels for the 2018 data set. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background prediction. The expected SM background distributions and the uncertainties are shown after fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a $\tau lepton$ for $\mathrm{m_{S}}$ = 1.2 TeV, before the fit, is also overlaid.
Observed and expected upper limits at 95%% CL on the production cross section for the type-III seesaw fermions in the flavor-democratic scenario using the table schemes and the BDT regions of the SS-M and the SS-H $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ BDTs. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.
Observed and expected upper limits at 95%% CL on the production cross section for the vector-like $\mathrm{\tau}$ leptons: doublet model. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks: $\mathrm{B_{\tau}=1}$ and $\mathrm{\beta=1}$. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks: $\mathrm{B_{e}=1}$ and $\mathrm{\beta=1}$. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks: $\mathrm{B_{\mu}=1}$ and $\mathrm{\beta=1}$. To the left of the vertical dashed gray line, the limits are shown from the advanced $\mathrm{S_{T}}$ table, and to the right the limits are shown from the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\mathrm{\tau}$ leptons: singlet model. The limit is shown from the advanced $\mathrm{S_{T}}$ table for all masses.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ scenario using the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=B_{\mu}=B_{\tau}}$ scenario using the Advanced $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\mu}=1}$ scenario using the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\mu}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\mu}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\mu}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=1}$ scenario using the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{e}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1}$ scenario using the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario using the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario using the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario using the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario using the Fundamental $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario using the Advanced $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the doublet scenario using the BDT regions.
Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the doublet scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the doublet scenario using the Fundamental $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the doublet scenario using the Advanced $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the singlet scenario using the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the singlet scenario using the Fundamental $\mathrm{S_{T}}$ table.
Observed and expected upper limits at 95% CL on the production cross section for the vector-like $\tau$ leptons in the singlet scenario using the Advanced $\mathrm{S_{T}}$ table.
Observed lower limits at 95% CL on the mass of the type-III seesaw fermions in the plane defined by $\mathrm{B_{e}}$ and $\mathrm{B_{\tau}}$, with the constraint that $\mathrm{B_{e}+B_{\mu}+B_{\tau}=1}$. These limits arise from the SS-H $\mathrm{B_{\tau}=1}$ BDT when $\mathrm{B_{\tau}\geq0.9}$, and by the SS-H $\mathrm{B_{e}+B_{\mu}+B_{\tau}=1}$ BDT for the other decay branching fraction combinations.
Median Expected lower limits at 95% CL on the mass of the type-III seesaw fermions in the plane defined by $\mathrm{B_{e}}$ and $\mathrm{B_{\tau}}$, with the constraint that $\mathrm{B_{e}+B_{\mu}+B_{\tau}=1}$. These limits arise from the SS-H $\mathrm{B_{\tau}=1}$ BDT when $\mathrm{B_{\tau}\geq0.9}$, and by the SS-H $\mathrm{B_{e}+B_{\mu}+B_{\tau}=1}$ BDT for the other decay branching fraction combinations.
Acceptance times efficiency values for the major SM backgrounds WZ, ZZ, and ttZ in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample. The statistical uncertainty on the acceptance times efficiency values is insignificant with respect to the quoted precision.
Acceptance times efficiency values with statistical uncertainty for the vector-like $\mathrm{\tau}$ lepton model in the doublet scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.
Acceptance times efficiency values with statistical uncertainty for the vector-like $\mathrm{\tau}$ lepton model in the singlet scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.
Acceptance times efficiency values with statistical uncertainty for the type-III seesaw fermions in the $\mathrm{(B_{e}=B_{\mu}=B_{\tau})}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.
Acceptance times efficiency values with statistical uncertainty for the type-III seesaw fermions in the $\mathrm{(B_{e}=1)}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.
Acceptance times efficiency values with statistical uncertainty for the type-III seesaw fermions in the $\mathrm{(B_{\mu}=1)}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.
Acceptance times efficiency values with statistical uncertainty for the type-III seesaw fermions in the $\mathrm{B_{\tau}=1)}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.
Acceptance times efficiency values with statistical uncertainty for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\tau}=1}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.
Acceptance times efficiency values with statistical uncertainty for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{e}=1}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.
Acceptance times efficiency values with statistical uncertainty for the scalar leptoquarks with $\mathrm{\beta=1}$ in the $\mathrm{B_{\mu}=1}$ scenario in the signal regions of all seven multilepton channels. The product is defined as the ratio of the total reconstructed yield in a given channel (after all the corrections and scale factor implementation) to the product of luminosity and the production cross section of the given simulation sample.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for electrons in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|<1.2}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of $\tau$ leptons for 0.2<dRmin<0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of $\tau$ leptons for dRmin>0.4. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for muons in the $\mathrm{|\eta|>1.2}$ region, arising from the decay of $\tau$ leptons for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 1-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|<1.1}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{|\eta|>1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for dRmin>0.2. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}<2}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
Reconstruction efficiency and associated uncertainty maps for 3-prong $\tau_{h}$ in the $\mathrm{1.1<|\eta|<1.6}$ region, arising from the decay of SM gauge bosons (W/Z/h) for $\mathrm{N_{j}>1}$. The lepton efficiency is estimated in a simulated event sample for the ZZ process. For a given input generator-level $\mathrm{p_{T}}$, the efficiency map provides the probability distribution of the reconstructed $\mathrm{p_{T}}$, accounting for reconstruction and identification efficiency, and the $\mathrm{p_{T}}$ resolution. The x-axis and the y-axis represent bins in the reconstructed and generated lepton $\mathrm{p_{T}}$, respectively.
The SR distributions of the Fundamental $\mathrm{L_{T}+p_{T}^{miss}}$ table for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the type-III seesaw heavy fermions in the flavor-democratic scenario for $\mathrm{m_{\Sigma}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the Fundamental $\mathrm{S_{T}}$ table for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the vector-like $\tau$ lepton in the doublet scenario for $\mathrm{m_{\tau'}}$ = 1 TeV, before the fit, is also overlaid.
The SR distributions of the Advanced $\mathrm{S_{T}}$ table for the combined 2016-2018 data set. The detailed description of the bin numbers can be found in Tables 3-6 in the paper. The lower panel shows the ratio of observed events to the total expected background prediction. The gray band on the ratio represents the sum of statistical and systematic uncertainties in the SM background predictions. The expected SM background distributions and the uncertainties are shown before fitting the data under the background-only hypothesis. For illustration, an example signal hypothesis for the production of the scalar leptoquark coupled to a top quark and a muon for $\mathrm{m_{S}}$ = 1.4 TeV, before the fit, is also overlaid.
A measurement of the forward-backward asymmetry of pairs of oppositely charged leptons (dimuons and dielectrons) produced by the Drell-Yan process in proton-proton collisions is presented. The data sample corresponds to an integrated luminosity of 138 fb$^{-1}$ collected with the CMS detector at the LHC at a center-of-mass energy of 13 TeV. The asymmetry is measured as a function of lepton pair mass for masses larger than 170\GeV and compared with standard model predictions. An inclusive measurement across both channels and the full mass range yields an asymmetry of 0.599 $\pm$ 0.005 (stat) $\pm$ 0.007 (syst). As a test of lepton flavor universality, the difference between the dimuon and dielectron asymmetries is measured as well. No statistically significant deviations from standard model predictions are observed. The measurements are used to set limits on the presence of additional gauge bosons. For a Z' in the sequential standard model, a lower mass limit of 4.4 TeV is set at 95% confidence level.
Results for the measurement of $A_\mathrm{FB}$ from the maximum likelihood fit to data in different dilepton mass bins in the different channels as well as an inclusive measurement across all mass bins.
Results for the measurement of $A_0$ from the maximum likelihood fit to data in different dilepton mass bins in the different channels as well as inclusive measurement across all mass bins. To help in the interpretation of these results, we also list the average dilepton $p_{T}$ of the data events in each mass bin.
Results for the measurement of $\Delta A_\mathrm{FB}$ and $\Delta A_0$ between the muon and electron channels from the maximum likelihood fit to data in different mass bins as well as an inclusive measurement across all mass bins.
The fraction of photon-induced background as compared with the total amount of DY signal plus photon-induced events ($N_{\gamma\gamma}/(N_{\gamma\gamma} + N_\mathrm{DY})$) in different dilepton mass bins. These numbers are averaged across the different years and channels.
Exclusion limits at 95% CL on the coupling K_L for a Z' in the sequential standard model as a function of the Z' mass.
Cross-section measurements of top-quark pair production where the hadronically decaying top quark has transverse momentum greater than $355$ GeV and the other top quark decays into $\ell \nu b$ are presented using 139 fb$^{-1}$ of data collected by the ATLAS experiment during proton-proton collisions at the LHC. The fiducial cross-section at $\sqrt{s}=13$ TeV is measured to be $\sigma = 1.267 \pm 0.005 \pm 0.053$ pb, where the uncertainties reflect the limited number of data events and the systematic uncertainties, giving a total uncertainty of $4.2\%$. The cross-section is measured differentially as a function of variables characterising the $t\bar{t}$ system and additional radiation in the events. The results are compared with various Monte Carlo generators, including comparisons where the generators are reweighted to match a parton-level calculation at next-to-next-to-leading order. The reweighting improves the agreement between data and theory. The measured distribution of the top-quark transverse momentum is used to set limits on the Wilson coefficients of the dimension-six operators $O_{tG}$ and $O_{tq}^{(8)}$ in the effective field theory framework.
- - - - - - - - Overview of HEPData Record - - - - - - - - <br/><br/> <b>Fiducial phase space definitions:</b><br/> <ul> <li> NLEP = 1, either E or MU, PT > 27 GeV, ABS ETA < 2.5 <li> NJETS >= 2, R = 0.4, PT > 26 GeV, ABS ETA < 2.5 <li> NBJETS >= 2 <li> NJETS >= 1, R=1, PT > 355 GeV, ABS ETA < 2.0, top-tagged </ul><br/> <u>1D:</u><br/> Spectra:<br/> <ul><br/> <li>SIG (<a href="1651136742?version=1&table=Table 1">Table 1</a> ) <li>DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 2">Table 2</a> ) <li>1/SIG*DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 4">Table 4</a> ) <li>DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 5">Table 5</a> ) <li>1/SIG*DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 7">Table 7</a> ) <li>DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 8">Table 8</a> ) <li>1/SIG*DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 10">Table 10</a> ) <li>DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 11">Table 11</a> ) <li>1/SIG*DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 13">Table 13</a> ) <li>DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 14">Table 14</a> ) <li>1/SIG*DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 16">Table 16</a> ) <li>DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 17">Table 17</a> ) <li>1/SIG*DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 19">Table 19</a> ) <li>DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 20">Table 20</a> ) <li>1/SIG*DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 22">Table 22</a> ) <li>DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 23">Table 23</a> ) <li>1/SIG*DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 25">Table 25</a> ) <li>DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 26">Table 26</a> ) <li>1/SIG*DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 28">Table 28</a> ) <li>DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 29">Table 29</a> ) <li>1/SIG*DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 31">Table 31</a> ) <li>DSIG/DHT (<a href="1651136742?version=1&table=Table 32">Table 32</a> ) <li>1/SIG*DSIG/DHT (<a href="1651136742?version=1&table=Table 34">Table 34</a> ) <li>DSIG/DNJETS (<a href="1651136742?version=1&table=Table 35">Table 35</a> ) <li>1/SIG*DSIG/DNJETS (<a href="1651136742?version=1&table=Table 37">Table 37</a> ) <li>DSIG/DPT_J1 (<a href="1651136742?version=1&table=Table 38">Table 38</a> ) <li>1/SIG*DSIG/DPT_J1 (<a href="1651136742?version=1&table=Table 40">Table 40</a> ) <li>DSIG/DM_J1_THAD (<a href="1651136742?version=1&table=Table 41">Table 41</a> ) <li>1/SIG*DSIG/DM_J1_THAD (<a href="1651136742?version=1&table=Table 43">Table 43</a> ) <li>DSIG/DDPHIOPI_THAD_J1 (<a href="1651136742?version=1&table=Table 44">Table 44</a> ) <li>1/SIG*DSIG/DDPHIOPI_THAD_J1 (<a href="1651136742?version=1&table=Table 46">Table 46</a> ) <li>DSIG/DDPHIOPI_THAD_J2 (<a href="1651136742?version=1&table=Table 47">Table 47</a> ) <li>1/SIG*DSIG/DDPHIOPI_THAD_J2 (<a href="1651136742?version=1&table=Table 49">Table 49</a> ) <li>DSIG/DDPHIOPI_J1_J2 (<a href="1651136742?version=1&table=Table 50">Table 50</a> ) <li>1/SIG*DSIG/DDPHIOPI_J1_J2 (<a href="1651136742?version=1&table=Table 52">Table 52</a> ) <li>DSIG/DPT_J2 (<a href="1651136742?version=1&table=Table 53">Table 53</a> ) <li>1/SIG*DSIG/DPT_J2 (<a href="1651136742?version=1&table=Table 55">Table 55</a> ) </ul><br/> Statistical covariance matrices: <ul> <li>DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 3">Table 3</a> ) <li>DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 6">Table 6</a> ) <li>DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 9">Table 9</a> ) <li>DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 12">Table 12</a> ) <li>DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 15">Table 15</a> ) <li>DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 18">Table 18</a> ) <li>DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 21">Table 21</a> ) <li>DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 24">Table 24</a> ) <li>DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 27">Table 27</a> ) <li>DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 30">Table 30</a> ) <li>DSIG/DHT (<a href="1651136742?version=1&table=Table 33">Table 33</a> ) <li>DSIG/DNJETS (<a href="1651136742?version=1&table=Table 36">Table 36</a> ) <li>DSIG/DPT_J1 (<a href="1651136742?version=1&table=Table 39">Table 39</a> ) <li>DSIG/DM_J1_THAD (<a href="1651136742?version=1&table=Table 42">Table 42</a> ) <li>DSIG/DDPHIOPI_THAD_J1 (<a href="1651136742?version=1&table=Table 45">Table 45</a> ) <li>DSIG/DDPHIOPI_THAD_J2 (<a href="1651136742?version=1&table=Table 48">Table 48</a> ) <li>DSIG/DDPHIOPI_J1_J2 (<a href="1651136742?version=1&table=Table 51">Table 51</a> ) <li>DSIG/DPT_J2 (<a href="1651136742?version=1&table=Table 54">Table 54</a> ) </ul><br/> Inter-spectra statistical covariance matrices: <ul> <li>Statistical covariance between DSIG/DPT_THAD and DSIG/DSIG (<a href="1651136742?version=1&table=Table 104">Table 104</a> ) <li>Statistical covariance between DSIG/DPT_TLEP and DSIG/DSIG (<a href="1651136742?version=1&table=Table 105">Table 105</a> ) <li>Statistical covariance between DSIG/DPT_TLEP and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 106">Table 106</a> ) <li>Statistical covariance between DSIG/DM_TTBAR and DSIG/DSIG (<a href="1651136742?version=1&table=Table 107">Table 107</a> ) <li>Statistical covariance between DSIG/DM_TTBAR and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 108">Table 108</a> ) <li>Statistical covariance between DSIG/DM_TTBAR and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 109">Table 109</a> ) <li>Statistical covariance between DSIG/DABS_Y_THAD and DSIG/DSIG (<a href="1651136742?version=1&table=Table 110">Table 110</a> ) <li>Statistical covariance between DSIG/DABS_Y_THAD and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 111">Table 111</a> ) <li>Statistical covariance between DSIG/DABS_Y_THAD and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 112">Table 112</a> ) <li>Statistical covariance between DSIG/DABS_Y_THAD and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 113">Table 113</a> ) <li>Statistical covariance between DSIG/DABS_Y_TLEP and DSIG/DSIG (<a href="1651136742?version=1&table=Table 114">Table 114</a> ) <li>Statistical covariance between DSIG/DABS_Y_TLEP and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 115">Table 115</a> ) <li>Statistical covariance between DSIG/DABS_Y_TLEP and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 116">Table 116</a> ) <li>Statistical covariance between DSIG/DABS_Y_TLEP and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 117">Table 117</a> ) <li>Statistical covariance between DSIG/DABS_Y_TLEP and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 118">Table 118</a> ) <li>Statistical covariance between DSIG/DY_TTBAR and DSIG/DSIG (<a href="1651136742?version=1&table=Table 119">Table 119</a> ) <li>Statistical covariance between DSIG/DY_TTBAR and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 120">Table 120</a> ) <li>Statistical covariance between DSIG/DY_TTBAR and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 121">Table 121</a> ) <li>Statistical covariance between DSIG/DY_TTBAR and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 122">Table 122</a> ) <li>Statistical covariance between DSIG/DY_TTBAR and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 123">Table 123</a> ) <li>Statistical covariance between DSIG/DY_TTBAR and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 124">Table 124</a> ) <li>Statistical covariance between DSIG/DHT_TTBAR and DSIG/DSIG (<a href="1651136742?version=1&table=Table 125">Table 125</a> ) <li>Statistical covariance between DSIG/DHT_TTBAR and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 126">Table 126</a> ) <li>Statistical covariance between DSIG/DHT_TTBAR and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 127">Table 127</a> ) <li>Statistical covariance between DSIG/DHT_TTBAR and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 128">Table 128</a> ) <li>Statistical covariance between DSIG/DHT_TTBAR and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 129">Table 129</a> ) <li>Statistical covariance between DSIG/DHT_TTBAR and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 130">Table 130</a> ) <li>Statistical covariance between DSIG/DHT_TTBAR and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 131">Table 131</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_BLEP and DSIG/DSIG (<a href="1651136742?version=1&table=Table 132">Table 132</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_BLEP and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 133">Table 133</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_BLEP and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 134">Table 134</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_BLEP and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 135">Table 135</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_BLEP and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 136">Table 136</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_BLEP and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 137">Table 137</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_BLEP and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 138">Table 138</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_BLEP and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 139">Table 139</a> ) <li>Statistical covariance between DSIG/DPT_TTBAR and DSIG/DSIG (<a href="1651136742?version=1&table=Table 140">Table 140</a> ) <li>Statistical covariance between DSIG/DPT_TTBAR and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 141">Table 141</a> ) <li>Statistical covariance between DSIG/DPT_TTBAR and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 142">Table 142</a> ) <li>Statistical covariance between DSIG/DPT_TTBAR and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 143">Table 143</a> ) <li>Statistical covariance between DSIG/DPT_TTBAR and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 144">Table 144</a> ) <li>Statistical covariance between DSIG/DPT_TTBAR and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 145">Table 145</a> ) <li>Statistical covariance between DSIG/DPT_TTBAR and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 146">Table 146</a> ) <li>Statistical covariance between DSIG/DPT_TTBAR and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 147">Table 147</a> ) <li>Statistical covariance between DSIG/DPT_TTBAR and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 148">Table 148</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DSIG (<a href="1651136742?version=1&table=Table 149">Table 149</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 150">Table 150</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 151">Table 151</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 152">Table 152</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 153">Table 153</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 154">Table 154</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 155">Table 155</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 156">Table 156</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 157">Table 157</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_TTBAR and DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 158">Table 158</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DSIG (<a href="1651136742?version=1&table=Table 159">Table 159</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 160">Table 160</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 161">Table 161</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 162">Table 162</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 163">Table 163</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 164">Table 164</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 165">Table 165</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 166">Table 166</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 167">Table 167</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 168">Table 168</a> ) <li>Statistical covariance between DSIG/DHT and DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 169">Table 169</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DSIG (<a href="1651136742?version=1&table=Table 170">Table 170</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 171">Table 171</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 172">Table 172</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 173">Table 173</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 174">Table 174</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 175">Table 175</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 176">Table 176</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 177">Table 177</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 178">Table 178</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 179">Table 179</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 180">Table 180</a> ) <li>Statistical covariance between DSIG/DNJETS and DSIG/DHT (<a href="1651136742?version=1&table=Table 181">Table 181</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DSIG (<a href="1651136742?version=1&table=Table 182">Table 182</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 183">Table 183</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 184">Table 184</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 185">Table 185</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 186">Table 186</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 187">Table 187</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 188">Table 188</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 189">Table 189</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 190">Table 190</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 191">Table 191</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 192">Table 192</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DHT (<a href="1651136742?version=1&table=Table 193">Table 193</a> ) <li>Statistical covariance between DSIG/DPT_J1 and DSIG/DNJETS (<a href="1651136742?version=1&table=Table 194">Table 194</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DSIG (<a href="1651136742?version=1&table=Table 195">Table 195</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 196">Table 196</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 197">Table 197</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 198">Table 198</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 199">Table 199</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 200">Table 200</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 201">Table 201</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 202">Table 202</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 203">Table 203</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 204">Table 204</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 205">Table 205</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DHT (<a href="1651136742?version=1&table=Table 206">Table 206</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DNJETS (<a href="1651136742?version=1&table=Table 207">Table 207</a> ) <li>Statistical covariance between DSIG/DM_J1_THAD and DSIG/DPT_J1 (<a href="1651136742?version=1&table=Table 208">Table 208</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DSIG (<a href="1651136742?version=1&table=Table 209">Table 209</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 210">Table 210</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 211">Table 211</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 212">Table 212</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 213">Table 213</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 214">Table 214</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 215">Table 215</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 216">Table 216</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 217">Table 217</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 218">Table 218</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 219">Table 219</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DHT (<a href="1651136742?version=1&table=Table 220">Table 220</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DNJETS (<a href="1651136742?version=1&table=Table 221">Table 221</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DPT_J1 (<a href="1651136742?version=1&table=Table 222">Table 222</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J1 and DSIG/DM_J1_THAD (<a href="1651136742?version=1&table=Table 223">Table 223</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DSIG (<a href="1651136742?version=1&table=Table 224">Table 224</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 225">Table 225</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 226">Table 226</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 227">Table 227</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 228">Table 228</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 229">Table 229</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 230">Table 230</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 231">Table 231</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 232">Table 232</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 233">Table 233</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 234">Table 234</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DHT (<a href="1651136742?version=1&table=Table 235">Table 235</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DNJETS (<a href="1651136742?version=1&table=Table 236">Table 236</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DPT_J1 (<a href="1651136742?version=1&table=Table 237">Table 237</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DM_J1_THAD (<a href="1651136742?version=1&table=Table 238">Table 238</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_THAD_J2 and DSIG/DDPHIOPI_THAD_J1 (<a href="1651136742?version=1&table=Table 239">Table 239</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DSIG (<a href="1651136742?version=1&table=Table 240">Table 240</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 241">Table 241</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 242">Table 242</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 243">Table 243</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 244">Table 244</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 245">Table 245</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 246">Table 246</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 247">Table 247</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 248">Table 248</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 249">Table 249</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 250">Table 250</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DHT (<a href="1651136742?version=1&table=Table 251">Table 251</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DNJETS (<a href="1651136742?version=1&table=Table 252">Table 252</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DPT_J1 (<a href="1651136742?version=1&table=Table 253">Table 253</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DM_J1_THAD (<a href="1651136742?version=1&table=Table 254">Table 254</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DDPHIOPI_THAD_J1 (<a href="1651136742?version=1&table=Table 255">Table 255</a> ) <li>Statistical covariance between DSIG/DDPHIOPI_J1_J2 and DSIG/DDPHIOPI_THAD_J2 (<a href="1651136742?version=1&table=Table 256">Table 256</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DSIG (<a href="1651136742?version=1&table=Table 257">Table 257</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DPT_THAD (<a href="1651136742?version=1&table=Table 258">Table 258</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DPT_TLEP (<a href="1651136742?version=1&table=Table 259">Table 259</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DM_TTBAR (<a href="1651136742?version=1&table=Table 260">Table 260</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DABS_Y_THAD (<a href="1651136742?version=1&table=Table 261">Table 261</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DABS_Y_TLEP (<a href="1651136742?version=1&table=Table 262">Table 262</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DY_TTBAR (<a href="1651136742?version=1&table=Table 263">Table 263</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DHT_TTBAR (<a href="1651136742?version=1&table=Table 264">Table 264</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DDPHIOPI_THAD_BLEP (<a href="1651136742?version=1&table=Table 265">Table 265</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DPT_TTBAR (<a href="1651136742?version=1&table=Table 266">Table 266</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DDPHIOPI_TTBAR (<a href="1651136742?version=1&table=Table 267">Table 267</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DHT (<a href="1651136742?version=1&table=Table 268">Table 268</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DNJETS (<a href="1651136742?version=1&table=Table 269">Table 269</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DPT_J1 (<a href="1651136742?version=1&table=Table 270">Table 270</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DM_J1_THAD (<a href="1651136742?version=1&table=Table 271">Table 271</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DDPHIOPI_THAD_J1 (<a href="1651136742?version=1&table=Table 272">Table 272</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DDPHIOPI_THAD_J2 (<a href="1651136742?version=1&table=Table 273">Table 273</a> ) <li>Statistical covariance between DSIG/DPT_J2 and DSIG/DDPHIOPI_J1_J2 (<a href="1651136742?version=1&table=Table 274">Table 274</a> ) </ul><br/> <u>2D:</u><br/> Spectra: <ul> <li>1/SIG*D2SIG/DPT_J1/DNJETS (NJETS = 1) (<a href="1651136742?version=1&table=Table 56">Table 56</a> ) <li>1/SIG*D2SIG/DPT_J1/DNJETS (NJETS = 2) (<a href="1651136742?version=1&table=Table 57">Table 57</a> ) <li>1/SIG*D2SIG/DPT_J1/DNJETS (NJETS $\geq$ 3) (<a href="1651136742?version=1&table=Table 58">Table 58</a> ) <li>D2SIG/DPT_J1/DNJETS (NJETS = 1) (<a href="1651136742?version=1&table=Table 59">Table 59</a> ) <li>D2SIG/DPT_J1/DNJETS (NJETS = 2) (<a href="1651136742?version=1&table=Table 60">Table 60</a> ) <li>D2SIG/DPT_J1/DNJETS (NJETS $\geq$ 3) (<a href="1651136742?version=1&table=Table 61">Table 61</a> ) <li>1/SIG*D2SIG/DPT_J1/DPT_THAD ( 355.0 GeV < PT_THAD < 398.0 GeV) (<a href="1651136742?version=1&table=Table 68">Table 68</a> ) <li>1/SIG*D2SIG/DPT_J1/DPT_THAD ( 398.0 GeV < PT_THAD < 496.0 GeV) (<a href="1651136742?version=1&table=Table 69">Table 69</a> ) <li>1/SIG*D2SIG/DPT_J1/DPT_THAD ( 496.0 GeV < PT_THAD < 2000.0 GeV) (<a href="1651136742?version=1&table=Table 70">Table 70</a> ) <li>D2SIG/DPT_J1/DPT_THAD ( 355.0 GeV < PT_THAD < 398.0 GeV) (<a href="1651136742?version=1&table=Table 71">Table 71</a> ) <li>D2SIG/DPT_J1/DPT_THAD ( 398.0 GeV < PT_THAD < 496.0 GeV) (<a href="1651136742?version=1&table=Table 72">Table 72</a> ) <li>D2SIG/DPT_J1/DPT_THAD ( 496.0 GeV < PT_THAD < 2000.0 GeV) (<a href="1651136742?version=1&table=Table 73">Table 73</a> ) <li>1/SIG*D2SIG/DDPHIOPI_THAD_J1/DPT_THAD ( 355.0 GeV < PT_THAD < 398.0 GeV) (<a href="1651136742?version=1&table=Table 80">Table 80</a> ) <li>1/SIG*D2SIG/DDPHIOPI_THAD_J1/DPT_THAD ( 398.0 GeV < PT_THAD < 496.0 GeV) (<a href="1651136742?version=1&table=Table 81">Table 81</a> ) <li>1/SIG*D2SIG/DDPHIOPI_THAD_J1/DPT_THAD ( 496.0 GeV < PT_THAD < 2000.0 GeV) (<a href="1651136742?version=1&table=Table 82">Table 82</a> ) <li>D2SIG/DDPHIOPI_THAD_J1/DPT_THAD ( 355.0 GeV < PT_THAD < 398.0 GeV) (<a href="1651136742?version=1&table=Table 83">Table 83</a> ) <li>D2SIG/DDPHIOPI_THAD_J1/DPT_THAD ( 398.0 GeV < PT_THAD < 496.0 GeV) (<a href="1651136742?version=1&table=Table 84">Table 84</a> ) <li>D2SIG/DDPHIOPI_THAD_J1/DPT_THAD ( 496.0 GeV < PT_THAD < 2000.0 GeV) (<a href="1651136742?version=1&table=Table 85">Table 85</a> ) <li>1/SIG*D2SIG/DDPHIOPI_THAD_J1/DNJETS (NJETS = 1) (<a href="1651136742?version=1&table=Table 92">Table 92</a> ) <li>1/SIG*D2SIG/DDPHIOPI_THAD_J1/DNJETS (NJETS = 2) (<a href="1651136742?version=1&table=Table 93">Table 93</a> ) <li>1/SIG*D2SIG/DDPHIOPI_THAD_J1/DNJETS (NJETS $\geq$ 3) (<a href="1651136742?version=1&table=Table 94">Table 94</a> ) <li>D2SIG/DDPHIOPI_THAD_J1/DNJETS (NJETS = 1) (<a href="1651136742?version=1&table=Table 95">Table 95</a> ) <li>D2SIG/DDPHIOPI_THAD_J1/DNJETS (NJETS = 2) (<a href="1651136742?version=1&table=Table 96">Table 96</a> ) <li>D2SIG/DDPHIOPI_THAD_J1/DNJETS (NJETS $\geq$ 3) (<a href="1651136742?version=1&table=Table 97">Table 97</a> ) </ul><br/> Statistical covariance matrices: <ul> <li>Statistical covariance matrix for D2SIG/DPT_J1/DNJETS between the 1st and 1st bins of NJETS (<a href="1651136742?version=1&table=Table 62">Table 62</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DNJETS between the 2nd and 1st bins of NJETS (<a href="1651136742?version=1&table=Table 63">Table 63</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DNJETS between the 2nd and 2nd bins of NJETS (<a href="1651136742?version=1&table=Table 64">Table 64</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DNJETS between the 3rd and 1st bins of NJETS (<a href="1651136742?version=1&table=Table 65">Table 65</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DNJETS between the 3rd and 2nd bins of NJETS (<a href="1651136742?version=1&table=Table 66">Table 66</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DNJETS between the 3rd and 3rd bins of NJETS (<a href="1651136742?version=1&table=Table 67">Table 67</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DPT_THAD between the 1st and 1st bins of PT_THAD (<a href="1651136742?version=1&table=Table 74">Table 74</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DPT_THAD between the 2nd and 1st bins of PT_THAD (<a href="1651136742?version=1&table=Table 75">Table 75</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DPT_THAD between the 2nd and 2nd bins of PT_THAD (<a href="1651136742?version=1&table=Table 76">Table 76</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DPT_THAD between the 3rd and 1st bins of PT_THAD (<a href="1651136742?version=1&table=Table 77">Table 77</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DPT_THAD between the 3rd and 2nd bins of PT_THAD (<a href="1651136742?version=1&table=Table 78">Table 78</a> ) <li>Statistical covariance matrix for D2SIG/DPT_J1/DPT_THAD between the 3rd and 3rd bins of PT_THAD (<a href="1651136742?version=1&table=Table 79">Table 79</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DPT_THAD between the 1st and 1st bins of PT_THAD (<a href="1651136742?version=1&table=Table 86">Table 86</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DPT_THAD between the 2nd and 1st bins of PT_THAD (<a href="1651136742?version=1&table=Table 87">Table 87</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DPT_THAD between the 2nd and 2nd bins of PT_THAD (<a href="1651136742?version=1&table=Table 88">Table 88</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DPT_THAD between the 3rd and 1st bins of PT_THAD (<a href="1651136742?version=1&table=Table 89">Table 89</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DPT_THAD between the 3rd and 2nd bins of PT_THAD (<a href="1651136742?version=1&table=Table 90">Table 90</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DPT_THAD between the 3rd and 3rd bins of PT_THAD (<a href="1651136742?version=1&table=Table 91">Table 91</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DNJETS between the 1st and 1st bins of NJETS (<a href="1651136742?version=1&table=Table 98">Table 98</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DNJETS between the 2nd and 1st bins of NJETS (<a href="1651136742?version=1&table=Table 99">Table 99</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DNJETS between the 2nd and 2nd bins of NJETS (<a href="1651136742?version=1&table=Table 100">Table 100</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DNJETS between the 3rd and 1st bins of NJETS (<a href="1651136742?version=1&table=Table 101">Table 101</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DNJETS between the 3rd and 2nd bins of NJETS (<a href="1651136742?version=1&table=Table 102">Table 102</a> ) <li>Statistical covariance matrix for D2SIG/DDPHIOPI_THAD_J1/DNJETS between the 3rd and 3rd bins of NJETS (<a href="1651136742?version=1&table=Table 103">Table 103</a> ) </ul><br/>
Total cross-section at particle level in the boosted topology. Note that the values shown here are obtained by propagating the individual uncertainties to the measured cross-sections, while the covariance matrices are evaluated using pseudo-experiments as described in the text. The measured cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $p_T^{t,h}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $p_T^{t,h}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $p_T^{t,l}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $p_T^{t,l}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $m^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $m^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $y^{t,h}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $y^{t,h}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $y^{t,l}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $y^{t,l}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $y^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $y^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $H_T^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $H_T^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $p_T^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $p_T^{t\bar{t}}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $N^j$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $N^j$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $N^j$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $p_T^{j,1}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $p_T^{j,1}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $m(j_1, t_h)$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $m(j_1, t_h)$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute differential cross-section as a function of $p_T^{j,2}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix of the Absolute differential cross-section as function of $p_T^{j,2}$ at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative differential cross-section as a function of $p_T^{j,2}$ at particle level in the boosted topology. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 1. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 2. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ $\geq$ 3. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 1. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 2. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $N^j$ at particle level in the boosted topology in $N^j$ $\geq$ 3. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 1 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 2 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 2 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 2 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ = 2 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $N^j$ in $N^j$ $\geq$ 3 at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Relative double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $p_T^{j,1}$ vs $p_T^{t,h}$ at particle level in the boosted topology in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $p_T^{j,1}$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ at particle level in the boosted topology in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 355.0 GeV < $p_T^{t,h}$ < 398.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 398.0 GeV < $p_T^{t,h}$ < 496.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $p_T^{t,h}$ in 496.0 GeV < $p_T^{t,h}$ < 2000.0 GeV at particle level in the boosted topology, accounting for the statistical uncertainties.
Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 1. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 2. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Relative double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ $\geq$ 3. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 1. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ = 2. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Absolute double-differential cross-section as a function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ at particle level in the boosted topology in $N^j$ $\geq$ 3. The measured differential cross-section is compared with the prediction obtained with the Powheg+Pythia8 Monte Carlo generator.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 1 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 2 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 2 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 2 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 1 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ = 2 at particle level in the boosted topology, accounting for the statistical uncertainties.
Covariance matrix between the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ $\geq$ 3 and the Absolute double-differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ vs $N^j$ in $N^j$ $\geq$ 3 at particle level in the boosted topology, accounting for the statistical uncertainties.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t,h}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t,l}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t,l}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m^{t\bar{t}}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,h}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,h}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,h}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,h}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t,l}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $y^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{t\bar{t}}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $N^j$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,1}$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $m(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ and the absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of Inclusive $\sigma$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $p_T^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $p_T^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $m^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $y^{t,h}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $y^{t,l}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $y^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(b_{l},t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $p_T^{t\bar{t}}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(t_{h},t_{l})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $H_T^{t\bar{t}+jets}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $N^j$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $p_T^{j,1}$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $m(j_1, t_h)$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_1, t_h)$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{2}, t_{h})$ at particle level in the boosted topology.
Statistical covariance matrix between the absolute differential cross-section as function of $p_T^{j,2}$ and the absolute differential cross-section as function of $\frac{\Delta\phi}{\pi}(j_{1}, j_{2})$ at particle level in the boosted topology.
A search is presented for a heavy W' boson resonance decaying to a B or T vector-like quark and a t or a b quark, respectively. The analysis is performed using proton-proton collisions collected with the CMS detector at the LHC. The data correspond to an integrated luminosity of 138 fb$^{-1}$ at a center-of-mass energy of 13 TeV. Both decay channels result in a signature with a t quark, a Higgs or Z boson, and a b quark, each produced with a significant Lorentz boost. The all-hadronic decays of the Higgs or Z boson and of the t quark are selected using jet substructure techniques to reduce standard model backgrounds, resulting in a distinct three-jet W' boson decay signature. No significant deviation in data with respect to the standard model background prediction is observed. Upper limits are set at 95% confidence level on the product of the W' boson cross section and the final state branching fraction. A W' boson with a mass below 3.1 TeV is excluded, given the benchmark model assumption of democratic branching fractions. In addition, limits are set based on generalizations of these assumptions. These are the most sensitive limits to date for this final state.
Reconstructed W′ boson mass distributions in the tHb signal region.
Reconstructed W′ boson mass distributions in the tZb signal region.
The W' boson 95% CL limits on the product of cross section and branching fraction. The expected and observed limits are shown for the center VLQ mass range.
The W' boson 95% CL limits on the product of cross section and branching fraction. The expected and observed limits are shown for the high VLQ mass range.
The W' boson 95% CL limits on the product of cross section and branching fraction. The expected and observed limits are shown for the low VLQ mass range.
Expected 95% CL limits for generalized hypotheses varying the fraction of tB (F(VLQ=B)) and bT (F(VLQ=T)) from the W′ decay
Expected 95% CL limits for generalized hypotheses varying the VLQ branching fraction to qH and qZ
Observed 95% CL limits for generalized hypotheses varying the fraction of tB (F(VLQ=B)) and bT (F(VLQ=T)) from the W′ decay
Observed 95% CL limits for generalized hypotheses varying the VLQ branching fraction to qH and qZ
A search for events with two displaced vertices from long-lived particles (LLP) pairs using data collected by the ATLAS detector at the LHC is presented. This analysis uses 139~fb$^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV recorded in 2015-2018. The search employs techniques for reconstructing vertices of LLPs decaying to jets in the muon spectrometer displaced between 3 m and 14 m with respect to the primary interaction vertex. The observed numbers of events are consistent with the expected background and limits for several benchmark signals are determined. For the Higgs boson with a mass of 125 GeV, the paper reports the first exclusion limits for branching fractions into neutral long-lived particles below 0.1%, while branching fractions above 10% are excluded at 95% confidence level for LLP proper lifetimes ranging from 4 cm to 72.4 m. In addition, the paper present the first results for the decay of LLPs into into $t\bar{t}$ in the ATLAS muon spectrometer.
Efficiency for the Muon RoI Cluster trigger as a function of the decay position of the LLP for some scalar portal samples in the MS barrel for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, “RPC 1/2” represent the first/second stations of RPC chambers, “TGC 1” represents the first stations of TGC chambers and “L/S” indicate whether they are in the Large or Small sectors.
Efficiency for the Muon RoI Cluster trigger as a function of the decay position of the LLP for some scalar portal samples in the MS endcaps for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, “RPC 1/2” represent the first/second stations of RPC chambers, “TGC 1” represents the first stations of TGC chambers and “L/S” indicate whether they are in the Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for scalar portal samples with $m_\varPhi=125$~\GeV\ for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, ``MDT 1/2'' represent the first/second stations of MDT chambers and ``L/S'' indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for scalar portal samples with $m_\varPhi=125$~\GeV\ for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, ``MDT 1/2'' represent the first/second stations of MDT chambers and ``L/S'' indicate whether they are in Large or Small sectors.
Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Efficiency for the Muon RoI Cluster trigger in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).
Efficiency for the Muon RoI Cluster trigger in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).
Efficiency for the Muon RoI Cluster trigger in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).
Efficiency for the Muon RoI Cluster trigger in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for non-SM Higgs benchmark samples for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling. The vertical lines show the relevant detector boundaries, where ``HCal end'' is the outer limit of the hadronic calorimeter, RPC 1/2 represent the first/second stations of RPC chambers, TGC 1 represents the first stations of TGC chambers and L/S indicate whether they are in the Large or Small sectors. The dependence on detector geometry can be seen by looking at the various detector boundaries depicted by dashed lines in the plots. In the barrel the trigger efficiency increases when the LLP decays are close to the end of the hadronic calorimeter (r ~ 4 m) and substantially decreases as the decay occurs closer to the middle station of the muon spectrometer (r ~ 7 m). For decays occurring close to the middle station the charged hadrons and photons (and their EM showers) are not spatially separated, resulting in low efficiency (the same reasoning is applicable to decays in the endcap regions).
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi = 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency of the Muon RoI Cluster trigger in the MS fiducial volume as a function of the LLP boost and decay position for scalar portal samples with $m_\varPhi \ne 125$ GeV for events passing the data quality requirements and having a reconstructed primary vertex. These efficiency distributions are based solely on MC simulation, without any corrections applied for mismodeling.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of transverse decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of longitudinal decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of transverse decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of longitudinal decay position of the LLP for non-SM Higgs benchmark samples for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS barrel fiducial volume as a function of the transverse decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS endcaps fiducial volume as a function of the longitudinal decay position of the LLP for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling. The vertical lines show the relevant detector boundaries, where “HCal end” is the outer limit of the hadronic calorimeter, MDT 1/2 represent the first/second stations of MDT chambers and L/S indicate whether they are in Large or Small sectors.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi = 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Efficiency to reconstruct an MS DV in the MS fiducial volume as a function of the LLP boost and decay position for a scalar portal sample with $m_\varPhi \ne 125$ GeV for vertices that pass the baseline event selection (except for the trigger) and satisfy the vertex isolation criteria. The efficiency distributions are corrected for mismodeling.
Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on ($\sigma / \sigma_{\text{SM}}) \times B$ for $m_\phi=125$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=60$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=60$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=200$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=400$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=600$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=600$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=600$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=1000$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=1000$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
Expected and observed 95% CL limits on $\sigma \times B$ for $m_\phi=1000$ GeV assuming 100% branching fraction of the long-lived scalar into fermion pairs.
A search for decays of pair-produced neutral long-lived particles (LLPs) is presented using 139 fb$^{-1}$ of proton-proton collision data collected by the ATLAS detector at the LHC in 2015-2018 at a centre-of-mass energy of 13 TeV. Dedicated techniques were developed for the reconstruction of displaced jets produced by LLPs decaying hadronically in the ATLAS hadronic calorimeter. Two search regions are defined for different LLP kinematic regimes. The observed numbers of events are consistent with the expected background, and limits for several benchmark signals are determined. For a SM Higgs boson with a mass of 125 GeV, branching ratios above 10% are excluded at 95% confidence level for values of $c$ times LLP mean proper lifetime in the range between 20 mm and 10 m depending on the model. Upper limits are also set on the cross-section times branching ratio for scalars with a mass of 60 GeV and for masses between 200 GeV and 1 TeV.
CalRatio triggers which were available during the LHC Run 2 data-taking, and corresponding integrated luminosity collected in each period. The high-E<sub>T</sub> CalRatio trigger with E<sub>T</sub> > 60 GeV was disabled in 2017 for instantaneous luminosities higher than 1.4 × 10<sup>34</sup> cm<sup>-2</sup> s<sup>-1</sup>. Two versions of the low-E<sub>T</sub> CalRatio trigger were used, with slight differences in their algorithms. The details are reported in Section 4.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP p<sub>T</sub> for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
Trigger efficiency for simulated signal events as a function of the LLP decay position in the x–y plane (L<sub>xy</sub>) for LLPs decaying in the barrel (|η|<1.4) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the high-E<sub>T</sub> L1 triggers with E<sub>T</sub> thresholds of 60 GeV and 100 GeV and by the two versions of the low-E<sub>T</sub> L1 triggers. Only statistical uncertainties are shown.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the low-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with no adversary network. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
The NN output scores in the dijet control region for the high-E<sub>T</sub> training with an adversary network included. Statistical uncertainties are shown in all plots. In cases where training with adversary networks is considered, the systematic uncertainty related to modelling discrepancies is included as well.
Distribution of the low-E<sub>T</sub> per-event BDT in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Distribution of the high-E<sub>T</sub> per-event BDT outputs in main data, BIB data and some of the benchmark signal samples after preselection. Only statistical uncertainties are shown.
Sequential impact of each requirement on the number of events passing the selection for the high-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Sequential impact of each requirement on the number of events passing the selection for the low-E<sub>T</sub> selections. The signal columns represent the cumulative fraction of events passing the selection than the number of events.
Application of the modified ABCD method to the final high-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
Application of the modified ABCD method to the final low-E<sub>T</sub> selections. The a priori estimate refers to the "pre-unblinding" case, where the data in region A are ignored by removing the Poisson constraint in that region and the signal strength is fixed to zero. This matches the simple N<sup>bkg</sup><sub>A</sub>=(N<sup>bkg</sup><sub>B</sub>· N<sup>bkg</sup><sub>C</sub>)/N<sup>bkg</sup><sub>D</sub> relation. The a posteriori estimate refers to the "post-unblinding" case, including the observed data in region A in the background-only global fit, obtained by fixing the signal strength to 0 (background-only fit) or allowing it to float (signal-plus-background fit). The table also shows one set of representative signal yields in each selection for the signal-plus-background fit. Only statistical uncertainties are included in the quoted error of the background, while the uncertainties in the signal include those from both statistical and experimental sources.
95% CL expected and observed limits on the BR of SM Higgs bosons to pairs of neutral LLPs (B<sub>H→ ss</sub>), showing the ± 1 σ (green) and ± 2 σ (yellow) expected limit bands, as well as a comparison with the results from previous ATLAS searches [36,78]. The cross-section for SM Higgs boson gluon--gluon fusion production is assumed to be 48.6 pb.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the low-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
Trigger efficiency of simulated signal events as a function of the LLP decay position in the z direction for LLPs decaying in the calorimeter endcaps (1.4 ≤ |η| < 2.5) for one of the high-E<sub>T</sub> signal samples for HLT CalRatio triggers seeded by the 60 GeV-high-E<sub>T</sub> trigger and by the two versions of the low-E<sub>T</sub> triggers.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The simulated efficiencies as a function of c times the mean proper lifetime (cτ) of s for several different MC samples. A weight-based extrapolation procedure is used to determine the efficiency at a given mean proper lifetime.
The event BDT in the dijet control region for the low-E<sub>T</sub> training training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The event BDT in the dijet control region for the high-E<sub>T</sub> training. The uncertainties are combined statistical and ML modelling systematic uncertainty.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 60 GeV compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 125 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available. The 125 GeV mediator is assumed to be the SM Higgs boson.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 200 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 400 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ mass of 600 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
The 95% CL observed limits, expected limits and ± 1 σ and 2 σ bands for the Φ masses of of 1000 GeV, compared to the results from the 2016-data analysis and subsequent combinations, where available.
Efficiency for an event to enter Region A of the high-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in decay position in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of p<sub>T</sub> bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the high-E<sub>T</sub> selections, for overall efficiencies above 0.5%, the results are typically accurate to around 25%, but below this the efficiency can be overestimated and therefore should not be used for re-interpration.
Efficiency for an event to enter Region A of the low-E<sub>T</sub> selection, as a function of the two LLP kinematics, decay type and decay position. LLP p<sub>T</sub> is binned in the ranges of [0, 50 , 100, 200 , 400, 1600] GeV (5 bins), the LLP decay position is binned in L<sub>xy</sub> in [0, 1.5, 2, 2.5, 3, 3.5, 3.9, ∞] m for LLPs with |η| < 1.5 and L<sub>z</sub> in [0, 3.6, 4.2, 4.8, 5.5, 6, ∞ ] m for LLPs with |η|geq1.5, 13 bins in all. Finally, four decay types are considered: LLPs decaying to pairs of c, b, t, or τ in bins 0, 1, 2, 3 respectively. The efficiency is presented as a function of "Bin Index", which is calculated as follows: Bin Index = (decay position bin index ) × (number of p<sub>T</sub> bins × number of decay type bins) + p<sub>T</sub> bin index * (number of decay type bin) + decay type bin index. The efficiency for a given pair-produced LLP sample can be obtained by summing the efficiency values for each event as extracted from this map, and dividing it by the total number of events in the sample. The efficiency map is symmetric between the LLPs, so the choice of LLP1 and LLP2 is arbitrary. For the low-E<sub>T</sub> selections, for overall efficiencies above 0.15%, the results are typically accurate to around 33%, and below this the efficiency is typically accurate up to a factor of 3.
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