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A search for heavy, long-lived, charged particles with large ionization energy loss within the silicon tracker of the CMS experiment is presented. A data set of proton-proton collisions at a center of mass energy at $\sqrt{s}$ = 13 TeV, collected in 2017 and 2018 at the CERN LHC, corresponding to an integrated luminosity of 101 fb$^{-1}$, is used in this analysis. Two different approaches for the search are taken. A new method exploits the independence of the silicon pixel and strips measurements, while the second method improves on previous techniques using ionization to determine a mass selection. No significant excess of events above the background expectation is observed. The results are interpreted in the context of the pair production of supersymmetric particles, namely gluinos, top squarks, and tau sleptons, and of the Drell-Yan pair production of fourth generation ($\tau'$) leptons with an electric charge equal to or twice the absolute value of the electron charge ($e$). An interpretation of a Z$'$ boson decaying to two $\tau'$ leptons with an electric charge equal to 2$e$ is presented for the first time. The 95% confidence upper limits on the production cross section are extracted for each of these hypothetical particles.
The $F_{\text{i}}^{\text{Pixels}}$ vs $G_{\text{i}}^{\text{Strips}}$ distribution for the SM MC after passing the selection criteria listed in Table 2.
The $F_{\text{i}}^{\text{Pixels}}$ vs $G_{\text{i}}^{\text{Strips}}$ distribution the 1800 GeV mass gluino R-hadron (right), after passing the selection criteria listed in Table 2.
The $G_{\text{i}}^{\text{Strips}}$ distribution in the FAIL region for events passing the event selection and with $55 < p_{\mathrm{T}} < 200$ GeV.
The $G_{\text{i}}^{\text{Strips}}$ distribution in the PASS region for events passing the event selection and with $55 < p_{\mathrm{T}} < 200$ GeV.
The $G_{\text{i}}^{\text{Strips}}$ distribution in the FAIL region for events passing the event selection and with $p_{\mathrm{T}} > 200$ GeV.
The $G_{\text{i}}^{\text{Strips}}$ distribution in the PASS region for events passing the event selection and with $p_{\mathrm{T}} > 200$ GeV.
Mass spectrum predicted in the signal region defined by $G_{\text{i}}^{\text{Strips}} > 0.22$ and $p_{\mathrm{T}} > 70$ GeV.
Cross section limits for gluino and supersymmetric top R-hadrons for both background prediction methods.
Cross section limits for supersymmetric tau models for both background prediction methods.
Cross section limits for DY-produced tau prime models (single and multicharged) for both background prediction methods.
Cross section limits for Z prime to multicharged tau prime model for both background prediction methods. All Z prime models assume a narrow width, and a 100% branching fraction to 600 GeV tau primes.
2D exclusion showing the observed cross section limit as a function of the multicharged tau prime mass and Z prime mass for the ionization method.
2D exclusion showing the observed cross section limit as a function of the multicharged tau prime mass and Z prime mass for the mass method.
Cumulative selection efficiency for the data and for two signal hypotheses.
Expected and observed mass limits obtained using 2017-2018 data for various HSCP candidate models,for the two background estimate methods.
Mass windows used in the mass method as a function of the signal target mass for the signal samples assuming a charge of $1e$ and $2e$, as appropriate to the signal model.
Mass spectrum predicted in the validation region defined by $0.018 < G_{\text{i}}^{\text{Strips}} < 0.057$ and $p_{\mathrm{T}} > 70$ GeV. The thresholds used in the $G_{\text{i}}^{\text{Strips}}$ requirement represent the 50% and 90% quantile of the distribution.
Trigger efficiency for the $\tilde{g}$ signals as a function of $\beta$ for $\text{abs(}\eta\text{)}<0.3$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{g}$ signals as a function of $\beta$ for $0.3<\text{abs(}\eta\text{)}<0.6$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{g}$ signals as a function of $\beta$ for $0.6<\text{abs(}\eta\text{)}<0.9$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{g}$ signals as a function of $\beta$ for $0.9<\text{abs(}\eta\text{)}<1.2$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{g}$ signals as a function of $\beta$ for $1.2<\text{abs(}\eta\text{)}<2.1$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{g}$ signals as a function of $\beta$ for $2.1<\text{abs(}\eta\text{)}<2.4$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{\tau}$ signals as a function of $\beta$ for $\text{abs(}\eta\text{)}<0.3$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{\tau}$ signals as a function of $\beta$ for $0.3<\text{abs(}\eta\text{)}<0.6$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{\tau}$ signals as a function of $\beta$ for $0.6<\text{abs(}\eta\text{)}<0.9$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{\tau}$ signals as a function of $\beta$ for $0.9<\text{abs(}\eta\text{)}<1.2$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{\tau}$ signals as a function of $\beta$ for $1.2<\text{abs(}\eta\text{)}<2.1$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency for the $\tilde{\tau}$ signals as a function of $\beta$ for $2.1<\text{abs(}\eta\text{)}<2.4$. The up and down variations are conservatively estimated assuming a delay of 1.5 ns in the muon chambers (equivalent to the time resolution of the chambers) and are used to evaluate the signal systematic uncertainties.
Trigger efficiency as a function of $\beta$, for the $\tilde{g}$ signals.
Trigger efficiency as a function of $\beta$, for the $\tilde{\tau}$ signals.
Cross section limits for $\tilde{g}$ R-hadrons obtained with the ionization method.
Cross section limits for $\tilde{g}$ R-hadrons obtained with the mass method.
Cross section limits for $\tilde{t}$ R-hadrons obtained with the ionization method.
Cross section limits for $\tilde{t}$ R-hadrons obtained with the mass method.
Cross section limits for $\tilde{\tau}$ obtained with the ionization method.
Cross section limits for $\tilde{\tau}$ obtained with the mass method.
Cross section limits for $\tilde{\tau}$ production within the GMSB SPS7 modelobtained with the ionization method.
Cross section limits for $\tilde{\tau}$ production within the GMSB SPS7 model obtained with the mass method.
Cross section limits for DY-produced $\tau'$ with $abs(Q) = 1e$ with the ionization method.
Cross section limits for DY-produced $\tau'$ with $abs(Q) = 1e$ with the mass method.
Cross section limits for DY-produced $\tau'$ with $abs(Q) = 2e$ with the ionization method.
Cross section limits for DY-produced $\tau'$ with $abs(Q) = 2e$ with the mass method.
Cross section limits for the production of $Z'$ boson decaying into a pair of $\tau'$ fermions of charge $2e$ (with a branching fraction equal to 1 and a fixed $\tau'$ mass of 600 GeV), obtained with the ionization method.
Cross section limits for the production of $Z'$ boson decaying into a pair of $\tau'$ fermions of charge $2e$ (with a branching fraction equal to 1 and a fixed $\tau'$ mass of 600 GeV), obtained with the mass method.
Two-dimensional exclusion showing the observed cross section limit as a function of the masses of the $\tau'$ (on the $x$ axis) and of the $Z'$ boson (on the $y$ axis), for the ionization method.
Two-dimensional exclusion showing the observed cross section limit as a function of the masses of the $\tau'$ (on the $x$ axis) and of the $Z'$ boson (on the $y$ axis), for the mass method.
Expected background yield, expected signal yield, and observed data for the mass method for 2017.
Expected background yield, expected signal yield, and observed data for the mass method for 2018.
Selection efficieny for GMSB supersymmetric $\tau$.
Selection efficiency for gluino R-hardon samples.
Selection efficiency for supersymmetric top R-hadrons.
Selection efficiency for pair-produced supersymmetric $\tau$.
Selection efficiency for $\tau'$ with $|Q| = 1e$.
Selection efficiecny for $\tau'$ with $|Q| = 2e$.
Selection efficiency for $Z'$ (mass = 3 TeV) going to $\tau'$ with $|Q| = 2e$.
Selection efficiency for $Z'$ (mass 5 TeV) goes to $\tau'$ with $|Q| = 2e$.
Selection efficiency for $Z'$ going to $\tau'$ (mass 600 GeV) with $|Q| = 2e$.
Selection efficiency for $Z'$ going to $\tau'$ (mass 1 TeV) with charge $|Q| = 2e$.
A combination of the results of several searches for the electroweak production of the supersymmetric partners of standard model bosons, and of charged leptons, is presented. All searches use proton-proton collision data at $\sqrt{s}$ = 13 TeV recorded with the CMS detector at the LHC in 2016-2018. The analyzed data correspond to an integrated luminosity of up to 137 fb$^{-1}$. The results are interpreted in terms of simplified models of supersymmetry. Two new interpretations are added with this combination: a model spectrum with the bino as the lightest supersymmetric particle together with mass-degenerate higgsinos decaying to the bino and a standard model boson, and the compressed-spectrum region of a previously studied model of slepton pair production. Improved analysis techniques are employed to optimize sensitivity for the compressed spectra in the wino and slepton pair production models. The results are consistent with expectations from the standard model. The combination provides a more comprehensive coverage of the model parameter space than the individual searches, extending the exclusion by up to 125 GeV, and also targets some of the intermediate gaps in the mass coverage.
Post-fit distribution of the $M(ll)$ variable for the low-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '2l soft' signal region of the '2/3l soft' analysis.
Post-fit distribution of the $M(ll)$ variable for the medium-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '2l soft' signal region of the '2/3l soft' analysis.
Post-fit distribution of the $M(ll)$ variable for the high-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '2l soft' signal region of the '2/3l soft' analysis.
Post-fit distribution of the $M(ll)$ variable for the ultrahigh-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '2l soft' signal region of the '2/3l soft' analysis.
Post-fit distribution of the $M(ll)$ variable for the low-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '3l soft' signal region of the the '2/3l soft' analysis.
Post-fit distribution of the $M(ll)$ variable for the medium-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '3l soft' signal region of the the '2/3l soft' analysis.
Post-fit distribution of the $m_{\mathrm{T2}}(ll)$ variable for low-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '2l soft' signal region of the '2/3l soft' analysis.
Post-fit distribution of the $m_{\mathrm{T2}}(ll)$ variable for medium-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '2l soft' signal region of the '2/3l soft' analysis.
Post-fit distribution of the $m_{\mathrm{T2}}(ll)$ variable for high-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '2l soft' signal region of the '2/3l soft' analysis.
Post-fit distribution of the $m_{\mathrm{T2}}(ll)$ variable for ultrahigh-$p_{\mathrm{T}}^{\mathrm{miss}}$ bins in the '2l soft' signal region of the '2/3l soft' analysis.
2SS $\ell/{\geq}\,3\ell$ search: observed and expected yields across the SRs in category A, events with three light leptons of which at least two form an OSSF pair, after the requirement that the leading-lepton $p_{\mathrm{T}}$ be greater than 30 GeV is applied.
2SS $\ell/{\geq}\,3\ell$ search: observed and expected yields across the SRs of the '${\geq}\ 3\ell$' search in category B, events with three light leptons and no OSSF pair, after the requirement that the leading-lepton $p_{\mathrm{T}}$ be greater than 30 GeV is applied.
Wino-bino model: cross section limits in the model parameter space, for wino-like chargino-neutralino production in the WZ topology for the full parameter space.
Wino-bino model: cross section limits in the model parameter space, for wino-like chargino-neutralino production in the WZ topology for the compressed space.
Wino-bino model: cross section limits in the model parameter space, for wino-like chargino-neutralino production in the WH topology for the full parameter space.
Wino-bino model: cross section limits in the model parameter space, for wino-like chargino-neutralino production with mixed topology with equal branching fraction to WZ and WH.
Wino-bino model: exclusion contours from the individual and combined analyses targeting WZ topology for the full parameter space. For visualization of the exclusion contours, linear interpolation is employed to account for the limited granularity of the available signal samples.
Wino-bino model: exclusion contours from the individual and combined analyses targeting the corresponding compressed region. For visualization of the exclusion contours, linear interpolation is employed to account for the limited granularity of the available signal samples.
Wino-bino model: exclusion contours from the individual and combined analyses targeting the WH topology for the full parameter space. For visualization of the exclusion contours, linear interpolation is employed to account for the limited granularity of the available signal samples.
Wino-bino model: exclusion contours from the individual and combined analyses targeting combined contours for these two topologies. For visualization of the exclusion contours, linear interpolation is employed to account for the limited granularity of the available signal samples.
GMSB model: expected and observed cross section limits for the neutralino-neutralino production for the ZZ topology.
GMSB model: expected and observed cross section limits for the neutralino-neutralino production for the HH topology.
GMSB model: expected and observed cross section limits for the neutralino-neutralino production for the mixed topology with equal branching fraction to H and Z.
GMSB model: cross section limits for neutralino-neutralino production as a function of the NSLP mass and the branching fraction to the H boson for the combination of the searches.
GMSB model: exclusion limit for neutralino-neutralino production as a function of the NSLP mass and the branching fraction to the H boson for the combination of the searches along with the input searches. For visualization of the exclusion contours, linear interpolation is employed to account for the limited granularity of the available signal samples.
Cross section upper limit(s) in the mass plane of NLSP and LSP masses for the higgsino-bino model.
Mass plane cross section upper limit for direct slepton pair production, with observed and expected exclusion limits in the full mass plane from the combination.
Mass plane cross section upper limit for direct slepton pair production, with observed and expected exclusion limits in the compressed region from '2/3l' soft search.
A search for long-lived particles decaying in the outer regions of the CMS silicon tracker or in the calorimeters is presented. The search is based on a data sample of proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded with the CMS detector at the LHC in 2016-2018, corresponding to an integrated luminosity of 138 fb$^{-1}$. A novel technique, using trackless and out-of-time jet information combined in a deep neural network discriminator, is employed to identify decays of long-lived particles. The results are interpreted in a simplified model of chargino-neutralino production, where the neutralino is the next-to-lightest supersymmetric particle, is long-lived, and decays to a gravitino and either a Higgs or Z boson. This search is most sensitive to neutralino proper decay lengths of approximately 0.5 m, for which masses up to 1.18 TeV are excluded at 95% confidence level. The current search is the best result to date in the mass range from the kinematic limit imposed by the Higgs mass up to 1.8 TeV.
Summary of combined statistical and systematic uncertainties, the size of their effect, and whether it applies to the signal or background yield predictions. Ranges for signal systematic uncertainties reflect their impact on different signal parameter space points.
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).
TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events.
TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events.
The efficiency of the TD jet tagger working point used in the analysis is shown as a function of the lab frame transverse decay length for simulated signals with $\chi$ mass of 400 GeV. The uncertainties shown account for lifetime dependence and statistical uncertainty.
The TD jet tagger score distributions for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. The last bin contains jets with tagger scores greater than 0.996, the threshold used to tag signal jets. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.
The TD jet tagger misidentification probability measured using the nominal $W$+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well ($Z$+jets MR as green round markers, $t\bar{t}$ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.
The TD jet tagger misidentification probability measured using the nominal $W$+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well ($Z$+jets MR as green round markers, $t\bar{t}$ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.
Distribution of the number of TD tagged jets for the $m_{\chi} = 400$ GeVsimulated signal samples with $c\tau_{\chi} = 0.5$ m (solid red line) and $c\tau_{\chi} = 3.0$ m (dotted green line), estimated background (blue square markers), and data (black round markers). The signal distributions are normalized to the expected cross section limit. The blue shaded region indicates the systematic uncertainty in the background prediction. No background prediction is shown for the bin with zero TD tagged jets as it is the main control region used to predict the background for the other two bins. There are zero observed events in the bin with two or more TD tagged jets.
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).
Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The distribution of the jet charged hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.
The distribution of the jet neutral hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.
The distribution of the number of track constituents in the jet, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.
The $\eta$ distribution of TD-tagged jets in a background-enriched control region that comprises events with exactly one TD-tagged jet. Observed data (black round markers) and the corresponding prediction based on control samples in data (empty squared markers), measured using the nominal $W$+jets MR, are compared. The prediction uncertainty (gray band) includes the systematic uncertainty quantifying the degree of process dependence measured from alternative MRs. The predictions for the shape and the normalization of the $\eta$ distribution are consistent with the data.
Jet time distribution in a sample of b-tagged jets from dilepton $t \bar{t}$ events in 2017 data-taking period (black round markers) and simulation (filled histogram). A Gaussian smearing procedure is applied to the jet time in the $t \bar{t}$ sample (green line) to correct for effects that are difficult to simulate (timing drift caused by crystals transparency loss due to detector aging, electronics jitter).
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 1$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.75$, $\mathcal{B}(\chi\to Z\tilde{G}) = 0.25$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.25$, $\mathcal{B}(\chi\to Z\tilde{G}) = 0.75$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to Z\tilde{G}) = 1$. The area enclosed by the dotted black line corresponds to the observed excluded region.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 127 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 127 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 150 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 150 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 175 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 175 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 200 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 200 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 250 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 250 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 300 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 300 GeV.
Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 400 GeV.
This paper presents a statistical combination of searches targeting final states with two top quarks and invisible particles, characterised by the presence of zero, one or two leptons, at least one jet originating from a $b$-quark and missing transverse momentum. The analyses are searches for phenomena beyond the Standard Model consistent with the direct production of dark matter in $pp$ collisions at the LHC, using 139 fb$^{-\text{1}}$ of data collected with the ATLAS detector at a centre-of-mass energy of 13 TeV. The results are interpreted in terms of simplified dark matter models with a spin-0 scalar or pseudoscalar mediator particle. In addition, the results are interpreted in terms of upper limits on the Higgs boson invisible branching ratio, where the Higgs boson is produced according to the Standard Model in association with a pair of top quarks. For scalar (pseudoscalar) dark matter models, with all couplings set to unity, the statistical combination extends the mass range excluded by the best of the individual channels by 50 (25) GeV, excluding mediator masses up to 370 GeV. In addition, the statistical combination improves the expected coupling exclusion reach by 14% (24%), assuming a scalar (pseudoscalar) mediator mass of 10 GeV. An upper limit on the Higgs boson invisible branching ratio of 0.38 (0.30$^{+\text{0.13}}_{-\text{0.09}}$) is observed (expected) at 95% confidence level.
Post-fit signal region yields for the tt0L-high and the tt0L-low analyses. The bottom panel shows the statistical significance of the difference between the SM prediction and the observed data in each region. '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.
Representative fit distribution in the signal region for the tt1L analysis: each bin of such distribution corresponds to a single SR included in the fit. 'Other' includes contributions from $t\bar{t}W$, $tZ$, $tWZ$ and $t\bar{t}$ (semileptonic) processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.
Representative fit distribution in the same flavour leptons signal region for the tt2L analysis: each bin of such distribution, starting from the red arrow, corresponds to a single SR included in the fit. 'FNP' includes the contribution from fake/non-prompt lepton background arising from jets (mainly $\pi/K$, heavy-flavour hadron decays and photon conversion) misidentified as leptons, estimated in a purely data-driven way. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.
Summary of the total uncertainty in the background prediction for each SR of the tt0L-low, tt0L-high, tt1L and tt2L analysis channels in the statistical combination. Their dominant contributions are indicated by individual lines. Individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.
Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.
Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.
$E_{\text{T}}^{\text{miss}}$ distribution in SR0X for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.
$E_{\text{T}}^{\text{miss}}$ distribution in SRWX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.
$E_{\text{T}}^{\text{miss}}$ distribution in SRTX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.
Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.
Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.
Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.
Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.
Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.
Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.
Representative fit distribution in the different flavour leptons signal region for the tt2L analysis: each bin of such distribution, starting from the red arrow, corresponds to a single SR included in the fit. 'FNP' includes the contribution from fake/non-prompt lepton background arising from jets (mainly $\pi/K$, heavy-flavour hadron decays and photon conversion) misidentified as leptons, estimated in a purely data-driven way. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.
Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.
Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.
Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.
Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of selected reconstructed events divided by the acceptance.
Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of selected reconstructed events divided by the acceptance.
Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of selected reconstructed events divided by the acceptance.
Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.
A search for the electroweak production of pairs of charged sleptons or charginos decaying into two-lepton final states with missing transverse momentum is presented. Two simplified models of $R$-parity-conserving supersymmetry are considered: direct pair-production of sleptons ($\tilde{\ell}\tilde{\ell}$), with each decaying into a charged lepton and a $\tilde{\chi}_1^0$ neutralino, and direct pair-production of the lightest charginos $(\tilde{\chi}_1^\pm\tilde{\chi}_1^\mp)$, with each decaying into a $W$-boson and a $\tilde{\chi}_1^0$. The lightest neutralino ($\tilde{\chi}_1^0$) is assumed to be the lightest supersymmetric particle (LSP). The analyses target the experimentally challenging mass regions where $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and $m(\tilde{\chi}_1^\pm)-m(\tilde{\chi}_1^0)$ are close to the $W$-boson mass (`moderately compressed' regions). The search uses 139 fb$^{-1}$ of $\sqrt{s}=13$ TeV proton-proton collisions recorded by the ATLAS detector at the Large Hadron Collider. No significant excesses over the expected background are observed. Exclusion limits on the simplified models under study are reported in the ($\tilde{\ell},\tilde{\chi}_1^0$) and ($\tilde{\chi}_1^\pm,\tilde{\chi}_1^0$) mass planes at 95% confidence level (CL). Sleptons with masses up to 150 GeV are excluded at 95% CL for the case of a mass-splitting between sleptons and the LSP of 50 GeV. Chargino masses up to 140 GeV are excluded at 95% CL for the case of a mass-splitting between the chargino and the LSP down to about 100 GeV.
<b>- - - - - - - - Overview of HEPData Record - - - - - - - -</b> <b>Title: </b><em>Search for direct pair production of sleptons and charginos decaying to two leptons and neutralinos with mass splittings near the $W$ boson mass in $\sqrt{s}=13$ TeV $pp$ collisions with the ATLAS detector</em> <b>Paper website:</b> <a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/SUSY-2019-02/">SUSY-2019-02</a> <b>Exclusion contours</b> <ul><li><b>Sleptons:</b> <a href=?table=excl_comb_obs_nominal>Combined Observed Nominal</a> <a href=?table=excl_comb_obs_up>Combined Observed Up</a> <a href=?table=excl_comb_obs_down>Combined Observed Down</a> <a href=?table=excl_comb_exp_nominal>Combined Expected Nominal</a> <a href=?table=excl_comb_exp_up>Combined Expected Up</a> <a href=?table=excl_comb_exp_down>Combined Expected Down</a> <a href=?table=excl_comb_obs_nominal_dM>Combined Observed Nominal $(\Delta m)$</a> <a href=?table=excl_comb_obs_up_dM>Combined Observed Up $(\Delta m)$</a> <a href=?table=excl_comb_obs_down_dM>Combined Observed Down $(\Delta m)$</a> <a href=?table=excl_comb_exp_nominal_dM>Combined Expected Nominal $(\Delta m)$</a> <a href=?table=excl_comb_exp_up_dM>Combined Expected Up $(\Delta m)$</a> <a href=?table=excl_comb_exp_down_dM>Combined Expected Down $(\Delta m)$</a> <a href=?table=excl_ee_obs_nominal>$\tilde{e}_\mathrm{L,R}$ Observed Nominal</a> <a href=?table=excl_ee_exp_nominal>$\tilde{e}_\mathrm{L,R}$ Expected Nominal</a> <a href=?table=excl_eLeL_obs_nominal>$\tilde{e}_\mathrm{L}$ Observed Nominal</a> <a href=?table=excl_eLeL_exp_nominal>$\tilde{e}_\mathrm{L}$ Expected Nominal</a> <a href=?table=excl_eReR_obs_nominal>$\tilde{e}_\mathrm{R}$ Observed Nominal</a> <a href=?table=excl_eReR_exp_nominal>$\tilde{e}_\mathrm{R}$ Expected Nominal</a> <a href=?table=excl_ee_obs_nominal_dM>$\tilde{e}_\mathrm{L,R}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_ee_exp_nominal_dM>$\tilde{e}_\mathrm{L,R}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_eLeL_obs_nominal_dM>$\tilde{e}_\mathrm{L}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_eLeL_exp_nominal_dM>$\tilde{e}_\mathrm{L}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_eReR_obs_nominal_dM>$\tilde{e}_\mathrm{R}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_eReR_exp_nominal_dM>$\tilde{e}_\mathrm{R}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_mm_obs_nominal>$\tilde{\mu}_\mathrm{L,R}$ Observed Nominal</a> <a href=?table=excl_mm_exp_nominal>$\tilde{\mu}_\mathrm{L,R}$ Expected Nominal</a> <a href=?table=excl_mLmL_obs_nominal>$\tilde{\mu}_\mathrm{L}$ Observed Nominal</a> <a href=?table=excl_mLmL_exp_nominal>$\tilde{\mu}_\mathrm{L}$ Expected Nominal</a> <a href=?table=excl_mRmR_obs_nominal>$\tilde{\mu}_\mathrm{R}$ Observed Nominal</a> <a href=?table=excl_mRmR_exp_nominal>$\tilde{\mu}_\mathrm{R}$ Expected Nominal</a> <a href=?table=excl_mm_obs_nominal_dM>$\tilde{\mu}_\mathrm{L,R}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_mm_exp_nominal_dM>$\tilde{\mu}_\mathrm{L,R}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_mLmL_obs_nominal_dM>$\tilde{\mu}_\mathrm{L}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_mLmL_exp_nominal_dM>$\tilde{\mu}_\mathrm{L}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_mRmR_obs_nominal_dM>$\tilde{\mu}_\mathrm{R}$ Observed Nominal $(\Delta m)$</a> <a href=?table=excl_mRmR_exp_nominal_dM>$\tilde{\mu}_\mathrm{R}$ Expected Nominal $(\Delta m)$</a> <a href=?table=excl_comb_obs_nominal_SR0j>Combined Observed Nominal SR-0j</a> <a href=?table=excl_comb_exp_nominal_SR0j>Combined Expected Nominal SR-0j</a> <a href=?table=excl_comb_obs_nominal_SR1j>Combined Observed Nominal SR-1j</a> <a href=?table=excl_comb_exp_nominal_SR1j>Combined Expected Nominal SR-1j</a> <li><b>Charginos:</b> <a href=?table=excl_c1c1_obs_nominal>Observed Nominal</a> <a href=?table=excl_c1c1_obs_up>Observed Up</a> <a href=?table=excl_c1c1_obs_down>Observed Down</a> <a href=?table=excl_c1c1_exp_nominal>Expected Nominal</a> <a href=?table=excl_c1c1_exp_nominal>Expected Up</a> <a href=?table=excl_c1c1_exp_nominal>Expected Down</a> <a href=?table=excl_c1c1_obs_nominal_dM>Observed Nominal $(\Delta m)$</a> <a href=?table=excl_c1c1_obs_up_dM>Observed Up $(\Delta m)$</a> <a href=?table=excl_c1c1_obs_down_dM>Observed Down $(\Delta m)$</a> <a href=?table=excl_c1c1_exp_nominal_dM>Expected Nominal $(\Delta m)$</a> <a href=?table=excl_c1c1_exp_nominal_dM>Expected Up $(\Delta m)$</a> <a href=?table=excl_c1c1_exp_nominal_dM>Expected Down $(\Delta m)$</a> </ul> <b>Upper Limits</b> <ul><li><b>Sleptons:</b> <a href=?table=UL_slep>ULs</a> <li><b>Charginos:</b> <a href=?table=UL_c1c1>ULs</a> </ul> <b>Pull Plots</b> <ul><li><b>Sleptons:</b> <a href=?table=pullplot_slep>SRs summary plot</a> <li><b>Charginos:</b> <a href=?table=pullplot_c1c1>SRs summary plot</a> </ul> <b>Cutflows</b> <ul><li><b>Sleptons:</b> <a href=?table=Cutflow_slep_SR0j>Towards SR-0J</a> <a href=?table=Cutflow_slep_SR1j>Towards SR-1J</a> <li><b>Charginos:</b> <a href=?table=Cutflow_SRs>Towards SRs</a> </ul> <b>Acceptance and Efficiencies</b> <ul><li><b>Sleptons:</b> <a href=?table=Acceptance_SR0j_MT2_100_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_100_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_110_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_110_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_120_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_120_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_130_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_130_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_100_105>SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_100_105>SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_105_110>SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_105_110>SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_110_115>SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_110_115>SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_115_120>SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_115_120>SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_120_125>SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_125_130>SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_130_140>SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_130_140>SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ Efficiency</a> <a href=?table=Acceptance_SR0j_MT2_140_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR0j_MT2_140_infty>SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_100_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[100,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_100_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[100,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_110_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[110,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_110_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[110,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_120_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[120,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_120_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[120,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_130_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[130,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_130_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[130,\infty)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_100_105>SR-1j $m_{\mathrm{T2}}^{100} \in[100,105)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_100_105>SR-1j $m_{\mathrm{T2}}^{100} \in[100,105)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_105_110>SR-1j $m_{\mathrm{T2}}^{100} \in[105,110)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_105_110>SR-1j $m_{\mathrm{T2}}^{100} \in[105,110)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_110_115>SR-1j $m_{\mathrm{T2}}^{100} \in[110,115)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_110_115>SR-1j $m_{\mathrm{T2}}^{100} \in[110,115)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_115_120>SR-1j $m_{\mathrm{T2}}^{100} \in[115,120)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_115_120>SR-1j $m_{\mathrm{T2}}^{100} \in[115,120)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_120_125>SR-1j $m_{\mathrm{T2}}^{100} \in[120,125)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_125_130>SR-1j $m_{\mathrm{T2}}^{100} \in[125,130)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_130_140>SR-1j $m_{\mathrm{T2}}^{100} \in[130,140)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_130_140>SR-1j $m_{\mathrm{T2}}^{100} \in[130,140)$ Efficiency</a> <a href=?table=Acceptance_SR1j_MT2_140_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[140,\infty)$ Acceptance</a> <a href=?table=Efficiency_SR1j_MT2_140_infty>SR-1j $m_{\mathrm{T2}}^{100} \in[140,\infty)$ Efficiency</a> <li><b>Charginos:</b> <a href=?table=Acceptance_SR_DF_81_1_SF_77_1>SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ Acceptance</a> <a href=?table=Efficiency_SR_DF_81_1_SF_77_1>SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ Efficiency</a> <a href=?table=Acceptance_SR_DF_81_1>SR-DF BDT-signal$\in(0.81,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_81_1>SR-DF BDT-signal$\in(0.81,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_82_1>SR-DF BDT-signal$\in(0.82,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_82_1>SR-DF BDT-signal$\in(0.82,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_83_1>SR-DF BDT-signal$\in(0.83,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_83_1>SR-DF BDT-signal$\in(0.83,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_84_1>SR-DF BDT-signal$\in(0.84,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_84_1>SR-DF BDT-signal$\in(0.84,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_85_1>SR-DF BDT-signal$\in(0.85,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_85_1>SR-DF BDT-signal$\in(0.85,1]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_81_8125>SR-DF BDT-signal$\in(0.81,8125]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_81_8125>SR-DF BDT-signal$\in(0.81,8125]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8125_815>SR-DF BDT-signal$\in(0.8125,815]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8125_815>SR-DF BDT-signal$\in(0.8125,815]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_815_8175>SR-DF BDT-signal$\in(0.815,8175]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_815_8175>SR-DF BDT-signal$\in(0.815,8175]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8175_82>SR-DF BDT-signal$\in(0.8175,82]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8175_82>SR-DF BDT-signal$\in(0.8175,82]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_82_8225>SR-DF BDT-signal$\in(0.82,8225]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_82_8225>SR-DF BDT-signal$\in(0.82,8225]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8225_825>SR-DF BDT-signal$\in(0.8225,825]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8225_825>SR-DF BDT-signal$\in(0.8225,825]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_825_8275>SR-DF BDT-signal$\in(0.825,8275]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_825_8275>SR-DF BDT-signal$\in(0.825,8275]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8275_83>SR-DF BDT-signal$\in(0.8275,83]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8275_83>SR-DF BDT-signal$\in(0.8275,83]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_83_8325>SR-DF BDT-signal$\in(0.83,8325]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_83_8325>SR-DF BDT-signal$\in(0.83,8325]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8325_835>SR-DF BDT-signal$\in(0.8325,835]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8325_835>SR-DF BDT-signal$\in(0.8325,835]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_835_8375>SR-DF BDT-signal$\in(0.835,8375]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_835_8375>SR-DF BDT-signal$\in(0.835,8375]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_8375_84>SR-DF BDT-signal$\in(0.8375,84]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_8375_84>SR-DF BDT-signal$\in(0.8375,84]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_84_845>SR-DF BDT-signal$\in(0.85,845]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_84_845>SR-DF BDT-signal$\in(0.85,845]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_845_85>SR-DF BDT-signal$\in(0.845,85]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_845_85>SR-DF BDT-signal$\in(0.845,85]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_85_86>SR-DF BDT-signal$\in(0.85,86]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_85_86>SR-DF BDT-signal$\in(0.85,86]$ Efficiency</a> <a href=?table=Acceptance_SR_DF_86_1>SR-DF BDT-signal$\in(0.86,1]$ Acceptance</a> <a href=?table=Efficiency_SR_DF_86_1>SR-DF BDT-signal$\in(0.86,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_77_1>SR-SF BDT-signal$\in(0.77,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_77_1>SR-SF BDT-signal$\in(0.77,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_78_1>SR-SF BDT-signal$\in(0.78,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_78_1>SR-SF BDT-signal$\in(0.78,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_79_1>SR-SF BDT-signal$\in(0.79,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_79_1>SR-SF BDT-signal$\in(0.79,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_80_1>SR-SF BDT-signal$\in(0.80,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_80_1>SR-SF BDT-signal$\in(0.80,1]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_77_775>SR-SF BDT-signal$\in(0.77,0.775]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_77_775>SR-SF BDT-signal$\in(0.77,0.775]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_775_78>SR-SF BDT-signal$\in(0.775,0.78]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_775_78>SR-SF BDT-signal$\in(0.775,0.78]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_78_785>SR-SF BDT-signal$\in(0.78,0.785]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_78_785>SR-SF BDT-signal$\in(0.78,0.785]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_785_79>SR-SF BDT-signal$\in(0.785,0.79]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_785_79>SR-SF BDT-signal$\in(0.785,0.79]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_79_795>SR-SF BDT-signal$\in(0.79,0.795]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_79_795>SR-SF BDT-signal$\in(0.79,0.795]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_795_80>SR-SF BDT-signal$\in(0.795,0.80]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_795_80>SR-SF BDT-signal$\in(0.795,0.80]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_80_81>SR-SF BDT-signal$\in(0.80,0.81]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_80_81>SR-SF BDT-signal$\in(0.80,0.81]$ Efficiency</a> <a href=?table=Acceptance_SR_SF_81_1>SR-SF BDT-signal$\in(0.81,1]$ Acceptance</a> <a href=?table=Efficiency_SR_SF_81_1>SR-SF BDT-signal$\in(0.81,1]$ Efficiency</a></ul> <b>Truth Code snippets</b>, <b>SLHA</b> and <b>machine learning</b> files are available under "Resources" (purple button on the left)
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-0J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.
Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-1J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.
Observed and expected exclusion limits on SUSY simplified models, with observed upper limits on signal cross-section (fb) overlaid, for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.
Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.
The upper panel shows the observed number of events in each of the binned SRs defined in Table 3, together with the expected SM backgrounds obtained after applying the efficiency correction method to compute the number of expected FSB events. `Others' include the non-dominant background sources, e.g. $t \bar{t}$+$V$, Higgs boson and Drell--Yan events. The uncertainty band includes systematic and statistical errors from all sources. The distributions of two signal points with mass splittings $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 30$ GeV and $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 50$ GeV are overlaid. The lower panel shows the significance as defined in Ref. [115].
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.785,0.79]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.785,0.79]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,0.795]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,0.795]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.795,0.80]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.795,0.80]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,0.81]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,0.81]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.
Cutflow table for the chargino signal sample with $m\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0=(125,25)$ GeV, in the SR-SF BDT-signal$\in (0.77,1]$ and SR-DF BDT-signal$\in (0.81,1]$ regions. The yields include the process cross-section and are weighted to the 139 fb$^{-1}$ luminosity. 170000 events were generated for the sample.
Observed and expected exclusion limits on SUSY simplified models, with observed upper limits on signal cross-section (fb) overlaid, for chargino-pair production with $W$-boson-mediated decays in the $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ plane. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.
The upper panel shows the observed number of events in the SRs defined in Table 3, together with the expected SM backgrounds obtained after the background fit in the CRs. `Others' include the non-dominant background sources, e.g.$t \bar{t}$+$V$, Higgs boson and Drell--Yan events. The uncertainty band includes systematic and statistical errors from all sources. Distributions for three benchmark signal points are overlaid for comparison. The lower panel shows the significance as defined in Ref. [115].
Three searches are presented for signatures of physics beyond the standard model (SM) in $\tau\tau$ final states in proton-proton collisions at the LHC, using a data sample collected with the CMS detector at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. Upper limits at 95% confidence level (CL) are set on the products of the branching fraction for the decay into $\tau$ leptons and the cross sections for the production of a new boson $\phi$, in addition to the H(125) boson, via gluon fusion (gg$\phi$) or in association with b quarks, ranging from $\mathcal{O}$(10 pb) for a mass of 60 GeV to 0.3 fb for a mass of 3.5 TeV each. The data reveal two excesses for gg$\phi$ production with local $p$-values equivalent to about three standard deviations at $m_\phi$ = 0.1 and 1.2 TeV. In a search for $t$-channel exchange of a vector leptoquark U$_1$, 95% CL upper limits are set on the dimensionless U$_1$ leptoquark coupling to quarks and $\tau$ leptons ranging from 1 for a mass of 1 TeV to 6 for a mass of 5 TeV, depending on the scenario. In the interpretations of the $M_\mathrm{h}^{125}$ and $M_\mathrm{h, EFT}^{125}$ minimal supersymmetric SM benchmark scenarios, additional Higgs bosons with masses below 350 GeV are excluded at 95% CL.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled. The peak in the expected $gg\phi$ limit is tribute to a loss of sensitivity around $90\text{ GeV}$ due to the background from $Z/\gamma^\ast\rightarrow\tau\tau$ events. Numerical values provided in this table correspond to Figure 10a of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $bb\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $gg\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 10b of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 37 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $bb\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $gg\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 38 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled and only top quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 39 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on the product of the cross sections and branching fraction for the decay into $\tau$ leptons for $gg\phi$ production in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$, in addition to $\text{H}(125)$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. In this case, $bb\phi$ production rate has been profiled and only bottom quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 40 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 31 of the auxilliary material of the publication.
Local significance for a $bb\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $gg\phi$ production rate has been profiled. Numerical values provided in this table correspond to Figure 32 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 33 of the auxilliary material of the publication.
Local significance for a $bb\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $gg\phi$ production rate has been fixed to zero. Numerical values provided in this table correspond to Figure 34 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled and only top quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 35 of the auxilliary material of the publication.
Local significance for a $gg\phi$ signal in a mass range of $60\leq m_\phi\leq 3500\text{ GeV}$. In this case, $bb\phi$ production rate has been profiled and only bottom quarks have been considered in the $gg\phi$ loop. Numerical values provided in this table correspond to Figure 36 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$), via vector boson fusion ($qq\phi$) or in association with b quarks ($bb\phi$). In this case, $bb\phi$ production rate is profiled, whereas the scan is performed in the $gg\phi$ and $qq\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 64 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $60\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 65 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $60\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 66 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $80\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 67 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $80\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 68 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $95\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 69 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $95\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 70 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $100\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 71 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $100\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 72 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $120\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 73 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $120\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 74 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $125\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 75 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $125\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 76 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $130\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 77 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $130\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 78 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $140\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 79 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $140\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 80 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $160\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 81 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $160\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 82 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $180\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 83 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $180\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 84 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a scalar resonance ($H$) with a mass of $200\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggH$ and $bbH$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{H}$ and the square root of the branching fraction for the $H\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $H$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 85 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a pseudoscalar resonance ($A$) with a mass of $200\text{ GeV}$, produced via gluon-fusion or in association with b quarks. For this scan, we assume the $ggA$ and $bbA$ processes are only influenced by the Yukawa couplings to the top and bottom quarks and we scale the cross sections predicted for a SM-like Higgs boson of the same mass depending on these couplings. For the $ggA$ process, there is also an enhancement to the cross section for a pseudoscalar resonance compared to the equivalent process for the production of a scalar. This enhancement is taken into account when scaling the cross sections for the SM-like Higgs boson. The scans are displayed for the product of the reduced Yukawa couplings $g_{b,\,t}^{A}$ and the square root of the branching fraction for the $A\rightarrow\tau\tau$ decay process, where the former is defined as the ratio of the Yukawa coupling of $A$ to the Yukawa coupling expected for a SM-like Higgs boson. Numerical values provided in this table correspond to Figure 86 of the auxilliary material of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 1 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 12a of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 2 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 12b of the publication.
Expected and observed $95\%\text{ CL}$ upper limits on $g_U$ in the VLQ BM 3 scenario in a mass range of $1\leq m_U\leq 5\text{ TeV}$. The central $68$ and $95\%$ intervals are given in addition to the expected median value. Numerical values provided in this table correspond to Figure 92 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $60\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11a of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $80\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 41 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 42 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $100\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11b of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $120\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 43 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $125\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11c of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $130\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 44 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $140\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 45 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $160\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11d of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $180\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 46 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 47 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $250\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11e of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 48 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $350\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 49 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 50 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $450\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 51 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11f of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 52 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $700\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 53 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 54 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 55 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11g of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11h of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 56 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 57 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 58 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 59 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 60 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 61 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 62 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 63 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11i of the publication.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $60\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11a of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $80\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 41 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $95\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 42 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $100\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11b of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $120\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 43 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $125\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11c of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $130\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 44 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $140\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 45 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $160\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11d of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $180\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 46 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 47 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $250\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11e of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 48 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $350\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 49 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 50 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $450\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 51 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11f of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 52 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $700\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 53 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 54 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 55 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11g of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11h of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1400\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 56 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 57 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $1800\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 58 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2000\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 59 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2300\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 60 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2600\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 61 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $2900\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 62 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3200\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 63 of the auxilliary material of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a resonance ($\phi$) with a mass of $3500\text{ GeV}$, produced via gluon-fusion ($gg\phi$) or in association with b quarks ($bb\phi$). The scan is performed in the $gg\phi$ and $bb\phi$ production cross-sections, both multiplied with the branching fraction for the $\phi\rightarrow\tau\tau$ decay process. Numerical values provided in this table correspond to Figure 11i of the publication, but evaluated on Asimov pseudodata.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 99 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 100 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 101 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 102 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 1 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 103 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 104 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 105 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 106 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 107 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 2 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 108 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 1\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 109 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 2\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 110 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 3\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 111 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 4\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 112 of the auxilliary material of the publication.
Scan of the likelihood function for the search for a vector leptoquark with $m_{U} = 5\text{ TeV}$, in the VLQ BM 3 scenario. The scan is performed in the $g_{U}$ coupling, for three different categorization strategies, combining only "No b tag" categories, only "b tag" categories, and all categories. Numerical values provided in this table correspond to Figure 113 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ quantile contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ quantile contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ quantile contour of Figure 13a of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ quantile contour of Figure 13a of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ quantile contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ quantile contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ quantile contour of Figure 13b of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ quantile contour of Figure 13b of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 114 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\tau})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 114 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 115 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\tilde{\chi})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 115 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 116 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{1}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 116 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 117 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{2}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 117 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 118 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_h^{125\,\mu_{3}-}$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 118 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 119 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h_{1}}^{125}(CPV)$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 119 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario. Numerical values provided in this table correspond to the observed contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 120 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM hMSSM scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 120 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 122 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h,\,\text{EFT}}^{125}(\tilde{\chi})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 122 of the auxilliary material of the publication.
Observed $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario. Numerical values provided in this table correspond to the observed contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the median of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. Numerical values provided in this table correspond to the expected median contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $16\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $16\%$ contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $84\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $68\%$ confidence interval band. Numerical values provided in this table correspond to the expected $84\%$ contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $2.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $2.5\%$ contour of Figure 123 of the auxilliary material of the publication.
Expected $95\%\text{ CL}$ exclusion contour in the MSSM $M_{h}^{125}(\text{alignment})$ scenario, evaluated at the $97.5\%$ quantile of the test-statistic distribution $f(\tilde{q}_\mu|\text{SM})$ under SM hypothesis. This contour is part of the $95\%$ confidence interval band. Numerical values provided in this table correspond to the expected $97.5\%$ contour of Figure 123 of the auxilliary material of the publication.
Fractions of the cross-section $\sigma(gg\phi)$ as expected from SM for the loop contributions with only top quarks, only bottom quarks and from the top-bottom interference. These values are used to scale the corresponding signal components for a given mass $m_\phi$.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for high-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 25 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8a of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 26 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8b of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 27 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 28 of the auxilliary material of the publication, but restricted to and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8c of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 29 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8d of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 30 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8e of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the high-mass analysis $m_{T}^{tot}$. Numerical values provided in this table correspond to Figure 8f of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the $t\bar{t}$ control region $m_{T}^{tot}$ for low-mass analysis. Numerical values provided in this table correspond to the $t\bar{t}$ control region of the publication, restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 11 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 12 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 13 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 14 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to High-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 10 of the auxilliary material of the publication, but restricted to Medium-$D_\zeta$ category and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $e\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 16 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 17 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 18 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 19 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 15 of the auxilliary material of the publication, but restricted to $\mu\tau_{h}$ final state and 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 21 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 22 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 23 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 24 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2016 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2017 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
Observed and expected distributions of the variable chosen for statistical inference in the low-mass analysis $m_{\tau\tau}$. Numerical values provided in this table correspond to Figure 20 of the auxilliary material of the publication, but restricted to 2018 data-taking year. All distributions are considered after a fit to data is performed using a background-only model, which includes the $\text{H}(125)$ boson. Some details on how the distributions should be used: 1) All given uncertainties correspond to systematic variations of $\pm1\sigma$. 2) Upper values ('plus' in the yaml file) correspond to an upward systematic variation of the parameter ($+1\sigma$). 3) Lower values ('minus' in the yaml file) correspond to a downward systematic variation of the parameter ($-1\sigma$). 4) These variations can have both positive and negative values, depending on the modelled effect. 5) Uncertainties with the same name should be treated as correlated, consistently across the upper and lower variations. 6) Systematic uncertainties with 'prop_' in the name treat limited background statistics per histogram bin, and are deployed with 'Barlow-Beeston-lite' approach. Details in https://arxiv.org/abs/1103.0354 section 5 7) Remaining systematic uncertainties alter the normalization, the shape, or both for a distribution. The nuisance parameter for such an uncertainty is mapped separately on the normalization and the shape variation components of the uncertainty. For normalization, $\ln$ mapping is used, for shape a spline. Details in https://cms-analysis.github.io/HiggsAnalysis-CombinedLimit/part2/settinguptheanalysis/#binned-shape-analysis 8) All nuisance parameters for the systematic uncertainties are modelled with a Gaussian pdf. 9) Gluon fusion contributions are all scaled to 1 pb. Please combine them using either the scale factors from 'Table SM Gluon Fusion Fractions', or using your own composition.
This paper presents a measurement of fiducial and differential cross-sections for $W^{+}W^{-}$ production in proton-proton collisions at $\sqrt{s}=13$ TeV with the ATLAS experiment at the Large Hadron Collider using a dataset corresponding to an integrated luminosity of 139 fb$^{-1}$. Events with exactly one electron, one muon and no hadronic jets are studied. The fiducial region in which the measurements are performed is inspired by searches for the electroweak production of supersymmetric charginos decaying to two-lepton final states. The selected events have moderate values of missing transverse momentum and the `stransverse mass' variable $m_{\textrm{T2}}$, which is widely used in searches for supersymmetry at the LHC. The ranges of these variables are chosen so that the acceptance is enhanced for direct $W^{+}W^{-}$ production and suppressed for production via top quarks, which is treated as a background. The fiducial cross-section and particle-level differential cross-sections for six variables are measured and compared with two theoretical SM predictions from perturbative QCD calculations.
Signal region detector-level distribution for the observable $|y_{e\mu}|$.
Signal region detector-level distribution for the observable $|\Delta \phi(e \mu)|$.
Signal region detector-level distribution for the observable $ \cos\theta^{\ast}$.
Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$.
Signal region detector-level distribution for the observable $m_{e\mu}$.
Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{e\mu}$.
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$
Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$
Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$
The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$
The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$
A search for new phenomena has been performed in final states with at least one isolated high-momentum photon, jets and missing transverse momentum in proton--proton collisions at a centre-of-mass energy of $\sqrt{s} = 13$ TeV. The data, collected by the ATLAS experiment at the CERN LHC, correspond to an integrated luminosity of 139 $fb^{-1}$. The experimental results are interpreted in a supersymmetric model in which pair-produced gluinos decay into neutralinos, which in turn decay into a gravitino, at least one photon, and jets. No significant deviations from the predictions of the Standard Model are observed. Upper limits are set on the visible cross section due to physics beyond the Standard Model, and lower limits are set on the masses of the gluinos and neutralinos, all at 95% confidence level. Visible cross sections greater than 0.022 fb are excluded and pair-produced gluinos with masses up to 2200 GeV are excluded for most of the NLSP masses investigated.
The observed and expected (post-fit) yields in the control and validation regions. The lower panel shows the difference in standard deviations between the observed and expected yields, considering both the systematic and statistical uncertainties on the background expectation.
Observed (points with error bars) and expected background (solid histograms) distributions for $E_{T}^{miss}$ in the signal region (a) SRL, (b) SRM and (c) SRH after the background-only fit applied to the CRs. The predicted signal distributions for the two models with a gluino mass of 2000 GeV and neutralino mass of 250 GeV (SRL), 1050 GeV (SRM) or 1950 GeV (SRH) are also shown for comparison. The uncertainties in the SM background are only statistical.
Observed (points with error bars) and expected background (solid histograms) distributions for $E_{T}^{miss}$ in the signal region (a) SRL, (b) SRM and (c) SRH after the background-only fit applied to the CRs. The predicted signal distributions for the two models with a gluino mass of 2000 GeV and neutralino mass of 250 GeV (SRL), 1050 GeV (SRM) or 1950 GeV (SRH) are also shown for comparison. The uncertainties in the SM background are only statistical.
Observed (points with error bars) and expected background (solid histograms) distributions for $E_{T}^{miss}$ in the signal region (a) SRL, (b) SRM and (c) SRH after the background-only fit applied to the CRs. The predicted signal distributions for the two models with a gluino mass of 2000 GeV and neutralino mass of 250 GeV (SRL), 1050 GeV (SRM) or 1950 GeV (SRH) are also shown for comparison. The uncertainties in the SM background are only statistical.
Observed and expected exclusion limit in the gluino-neutralino mass plane at 95% CL combined using the signal region with the best expected sensitivity at each point, for the full Run-2 dataset corresponding to an integrated luminosity of $139~\mathrm{fb}^{-1}$, for $\gamma/Z$ (a) and $\gamma/h$ (b) signal models. The black solid line corresponds to the expected limits at 95% CL, with the light (yellow) bands indicating the 1$\sigma$ exclusions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves, the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties. For each point in the higgsino-bino parameter space, the labels indicate the best-expected signal region, where L, M and H mean SRL, SRM and SRH, respectively.
Observed and expected exclusion limit in the gluino-neutralino mass plane at 95% CL combined using the signal region with the best expected sensitivity at each point, for the full Run-2 dataset corresponding to an integrated luminosity of $139~\mathrm{fb}^{-1}$, for $\gamma/Z$ (a) and $\gamma/h$ (b) signal models. The black solid line corresponds to the expected limits at 95% CL, with the light (yellow) bands indicating the 1$\sigma$ exclusions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves, the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties. For each point in the higgsino-bino parameter space, the labels indicate the best-expected signal region, where L, M and H mean SRL, SRM and SRH, respectively.
Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).
Cutflow for the SRL selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 250 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.
Cutflow for the SRM selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 1050 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.
Cutflow for the SRH selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 1950 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.
This Letter presents a search for direct production of charginos and neutralinos via electroweak interactions. The results are based on data from proton-proton collisions at a center-of-mass energy of 13 TeV collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 137 fb$^{-1}$. The search considers final states with large missing transverse momentum and pairs of hadronically decaying bosons WW, WZ, and WH, where H is the Higgs boson. These bosons are identified using novel algorithms. No significant excess of events is observed relative to the expectations from the standard model. Limits at the 95% confidence level are placed on the cross section for production of mass-degenerate wino-like supersymmetric particles $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$, and mass-degenerate higgsino-like supersymmetric particles $\tilde{\chi}_1^\pm$, $\tilde{\chi}_2^0$, and $\tilde{\chi}_3^0$. In the limit of a nearly-massless lightest supersymmetric particle $\tilde{\chi}_1^0$, wino-like particles with masses up to 870 and 960 GeV are excluded in the cases of $\tilde{\chi}_2^0$ $\to$ Z$\tilde{\chi}_1^0$ and $\tilde{\chi}_2^0$ $\to$ H$\tilde{\chi}_1^0$, respectively, and higgsino-like particles are excluded between 300 and 650 GeV.
SM background prediction vs. observation in the b-veto signal region
SM background observation/prediction in the bVeto signal region
SM background prediction vs. observation in the WH signal region
SM background observation/prediction in the WH signal region
SM background prediction vs. observation in the W signal region
SM background observation/prediction in the W signal region
SM background prediction vs. observation in the H signal region
SM background observation/prediction in the H signal region
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected exclusion limits assuming the approximate-NLO+NLL cross sections
The 95% CL observed upper limits on the production cross sections for $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^\mp_1$ assuming that each $\widetilde{\chi}^\pm_1$ decays to a W boson and $\widetilde{\chi}^0_1$
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected exclusion limits assuming the approximate-NLO+NLL cross sections
The 95% CL observed upper limits on the production cross sections for $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^0_2$ assuming that each $\widetilde{\chi}^\pm_1$ decays to a W boson and $\widetilde{\chi}^0_1$ and the $\widetilde{\chi}^0_2$ decays to a Z boson and $\widetilde{\chi}^0_1$
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected exclusion limits assuming the approximate-NLO+NLL cross sections
The 95% CL observed upper limits on the production cross sections for $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^0_2$ assuming that each $\widetilde{\chi}^\pm_1$ decays to a W boson and $\widetilde{\chi}^0_1$ and the $\widetilde{\chi}^0_2$ decays to a H boson and $\widetilde{\chi}^0_1$
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected exclusion limits assuming the approximate-NLO+NLL cross sections
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected exclusion limits assuming the approximate-NLO+NLL cross sections
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected exclusion limits assuming the approximate-NLO+NLL cross sections
The 95% CL observed upper limits on the production cross sections for mass-degenerate higgsino-like$\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^\mp_1$, $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^0_2$, $\widetilde{\chi}^\pm_1$ $\widetilde{\chi}^0_3$ and $\widetilde{\chi}^0_2$ $\widetilde{\chi}^0_3$ as functions of the NLSP and LSP masses.
Efficiency of bb-tagger for H(bb), Z(bb) and Z(cc) decays.
Efficiency of W- and V-tagger for W(qq) and Z(qq) decays.
Acceptance times efficiency values with statistical uncertainties for TChiWW in the b-Veto region.
Acceptance times efficiency values with statistical uncertainties for TChiWZ in the b-Veto region.
Acceptance times efficiency values with statistical uncertainties for TChiWH in the b-Veto region.
Acceptance times efficiency values with statistical uncertainties for TChiHZ in the b-Veto region.
Acceptance times efficiency values with statistical uncertainties for TChiWW in the WHSR region.
Acceptance times efficiency values with statistical uncertainties for TChiWW in the WSR region.
Acceptance times efficiency values with statistical uncertainties for TChiWW in the HSR region.
Acceptance times efficiency values with statistical uncertainties for TChiWZ in the WHSR region.
Acceptance times efficiency values with statistical uncertainties for TChiWZ in the WSR region.
Acceptance times efficiency values with statistical uncertainties for TChiWZ in the HSR region.
Acceptance times efficiency values with statistical uncertainties for TChiWH in the WHSR region.
Acceptance times efficiency values with statistical uncertainties for TChiWH in the WSR region.
Acceptance times efficiency values with statistical uncertainties for TChiWH in the HSR region.
Acceptance times efficiency values with uncertainties for TChiHZ in the WHSR region.
Acceptance times efficiency values with uncertainties for TChiHZ in the WSR region.
Acceptance times efficiency values with statistical uncertainties for TChiHZ in the HSR region.
Covariance matrix for the signal regions, derived from a fit to the control regions only under the background-only hypothesis.
Correlation matrix for the signal regions, derived from a fit to the control regions only under the background-only hypothesis.
A search is reported for heavy resonances and quantum black holes decaying into e$\mu$, e$\tau$, and $\mu\tau$ final states in proton-proton collision data recorded by the CMS experiment at the CERN LHC during 2016-2018 at $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb$^{-1}$. The e$\mu$, e$\tau$, and $\mu\tau$ invariant mass spectra are reconstructed, and no evidence is found for physics beyond the standard model. Upper limits are set at 95% confidence level on the product of the cross section and branching fraction for lepton flavor violating signals. Three benchmark signals are studied: resonant $\tau$ sneutrino production in $R$ parity violating supersymmetric models, heavy Z' gauge bosons with lepton flavor violating decays, and nonresonant quantum black hole production in models with extra spatial dimensions. Resonant $\tau$ sneutrinos are excluded for masses up to 4.2 TeV in the e$\mu$ channel, 3.7 TeV in the e$\tau$ channel, and 3.6 TeV in the $\mu\tau$ channel. A Z' boson with lepton flavor violating couplings is excluded up to a mass of 5.0 TeV in the e$\mu$ channel, up to 4.3 TeV in the e$\tau$ channel, and up to 4.1 TeV in the $\mu\tau$ channel. Quantum black holes in the benchmark model are excluded up to the threshold mass of 5.6 TeV in the e$\mu$ channel, 5.2 TeV in the e$\tau$ channel, and 5.0 TeV in the $\mu\tau$ channel. In addition, model-independent limits are extracted to allow comparisons with other models for the same final states and similar event selection requirements. The results of these searches provide the most stringent limits available from collider experiments for heavy particles that undergo lepton flavor violating decays.
Mass distributions for the e$\mu$ channel. In addition to the observed data (black points) and SM prediction (filled histograms), expected signal distributions for three models are shown: the RPV SUSY model with $\lambda = \lambda' = 0.01$ and $\tau$ sneutrino mass of 1.6 TeV, a Z′ boson ($\mathcal{B}=0.1$) with a mass of 1.6 TeV , and the QBH signal expectation for $n=4$ and a threshold mass of 1.6 TeV. The bin width gradually increases with mass.
Mass distributions for the e$\tau$ channel. In addition to the observed data (black points) and SM prediction (filled histograms), expected signal distributions for three models are shown: the RPV SUSY model with $\lambda = \lambda' = 0.01$ and $\tau$ sneutrino mass of 1.6 TeV, a Z′ boson ($\mathcal{B}=0.1$) with a mass of 1.6 TeV , and the QBH signal expectation for $n=4$ and a threshold mass of 1.6 TeV. The bin width gradually increases with mass.
Mass distributions for the $\mu\tau$ channel. In addition to the observed data (black points) and SM prediction (filled histograms), expected signal distributions for three models are shown: the RPV SUSY model with $\lambda = \lambda' = 0.01$ and $\tau$ sneutrino mass of 1.6 TeV, a Z′ boson ($\mathcal{B}=0.1$) with a mass of 1.6 TeV , and the QBH signal expectation for $n=4$ and a threshold mass of 1.6 TeV. The bin width gradually increases with mass.
The observed and expected (in parentheses) 95% CL lower mass limits on RPV SUSY, Z′ ($\mathcal{B}=0.1$) , and QBH signals for the e$\mu$, e$\tau$, and $\mu\tau$ channels.
Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.
Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.
Expected and observed 95% CL upper limits on the product of cross section times branching fraction as a function of the $ au$ sneutrino mass in an RPV SUSY model for the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red and blue solid lines show the product of cross section times branching fraction as a function of the tau sneutrino mass for two different values of couplings.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the e$\mu$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the e$\tau$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for a Z′ ($\mathcal{B}=0.1$) boson with LFV decays, in the $\mu\tau$ channel.The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the Z′ mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.
Expected (black dashed line) and observed (black solid line) 95% CL upper limits on the product of cross section and branching fraction for quantum black hole production in an ADD model with $n=4$ extra dimensions, in the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits. The red solid lines show the product of cross section times branching fraction as a function of the QBH threshold mass.
Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the e$\mu$ channel. The regions to the left of and above the curves are excluded.
Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the e$\tau$ channel. The regions to the left of and above the curves are excluded.
Upper limits at 95% CL on the RPV SUSY model in the plane of $\tau$ sneutrino mass and $\lambda'$ coupling, for four values of $\lambda$ couplings for the $\mu\tau$ channel. The regions to the left of and above the curves are excluded.
Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the e$\mu$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.
Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the e$\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.
Model-independent upper limits at 95% CL on the product of cross section, branching fraction, and acceptance are shown. Observed (expected) limits are shown in black solid (dashed) lines for the $\mu\tau$ channel. The shaded bands represent the one and two standard deviation (s.d.) uncertainties in the expected limits.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.
Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning from Figure 2.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.
Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit and serve as input to the simplified likelihood reinterpretation scheme. The naming of the bins is "channel_year_binnumber", following the binning used in Figure 2.
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