Number of events for the different lepton flavour categories in the signal region accounting for the fit to data. The hatched band includes all systematic uncertainties in the MC prediction. The vertical bars of the data account for the statistical uncertainty. The ratio panels show the ratio between data (black markers) with respect to the total prediction after the fit to data. Processes with a small contribution to this region are grouped in the ``Other" category

Sum of the charge of the final-state leptons in the signal region accounting for the fit to data. This distribution is not included in the fit. The hatched band includes all systematic uncertainties in the MC prediction. The vertical bars of the data account for the statistical uncertainty. The ratio panels show the ratio between data (black markers) with respect to the total prediction after the fit to data. Processes with a small contribution to this region are grouped in the ``Other" category.

Invariant mass of the trilepton system in the signal region accounting for the fit to data. This distribution is not included in the fit. The hatched band includes all systematic uncertainties in the MC prediction. The vertical bars of the data account for the statistical uncertainty. The ratio panels show the ratio between data (black markers) with respect to the total prediction after the fit to data. Processes with a small contribution to this region are grouped in the ``Other" category

Measured fiducial cross sections and their corresponding uncertainties for the flavourexclusive and flavour-inclusive categories. The predictions from both POWHEG at NLO in QCD and LO EW as well as several ones obtained from MATRIX (NLO QCD, NNLO QCD, NNLO QCD × NLO EW) are also included

Total WZ production cross section for each of the flavour-exclusive and for the flavour-inclusive categories. The vertical bands show different theoretical predictions for the WZ cross section at NLO in QCD (red dashed line) and NNLO QCD × NLO EW (blue solid line), as well as their corresponding scale uncertainties. For each measurement, the best fit value is denoted with a purple point, with two delimiters on the error bars that account for the total and statistical uncertainties

Observed two-dimensional likelihood scans for $c_{Hq}^{(1)}$ vs. $c_{Hq}^{(3)}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hq}^{(1)}$ vs. $c_{Hu}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hq}^{(1)}$ vs. $c_{Hu}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hq}^{(1)}$ vs. $c_{Hd}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hq}^{(1)}$ vs. $c_{Hd}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hq}^{(3)}$ vs. $c_{Hu}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hq}^{(3)}$ vs. $c_{Hu}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hq}^{(3)}$ vs. $c_{Hd}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hq}^{(3)}$ vs. $c_{Hd}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hu}$ vs. $c_{Hd}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hu}$ vs. $c_{Hd}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hq}^{(1)}$ vs. $g^{(2)}_{ZZ}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hq}^{(1)}$ vs. $g^{(2)}_{ZZ}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hq}^{(3)}$ vs. $g^{(2)}_{ZZ}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hq}^{(3)}$ vs. $g^{(2)}_{ZZ}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hu}$ vs. $g^{(2)}_{ZZ}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hu}$ vs. $g^{(2)}_{ZZ}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hq}^{(1)}$ vs. $g^{(4)}_{ZZ}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hq}^{(1)}$ vs. $g^{(4)}_{ZZ}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hq}^{(3)}$ vs. $g^{(4)}_{ZZ}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hq}^{(3)}$ vs. $g^{(4)}_{ZZ}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hu}$ vs. $g^{(4)}_{ZZ}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hu}$ vs. $g^{(4)}_{ZZ}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hd}$ vs. $g^{(2)}_{ZZ}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hd}$ vs. $g^{(2)}_{ZZ}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $c_{Hd}$ vs. $g^{(4)}_{ZZ}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $c_{Hd}$ vs. $g^{(4)}_{ZZ}$ while other coefficients are fixed at their SM values.

Observed two-dimensional likelihood scans for $g^{(2)}_{ZZ}$ vs. $g^{(4)}_{ZZ}$ while allowing the other coefficients to float freely at each point of the sca.

Observed two-dimensional likelihood scans for $g^{(2)}_{ZZ}$ vs. $g^{(4)}_{ZZ}$ while other coefficients are fixed at their SM values.

Distributions in $m_{Z'}^{rec}$ for data in the SRs, together with fits of the background functions under the background-only hypothesis for the muon channel. The number of observed events in each bin is divided by the bin width. The signal predictions are shown for different Z' boson masses, normalized to an arbitrary cross section of 1 fb. In the panels below the distributions, the ratios of data to the background function are displayed. The shaded green areas represent the statistical uncertainty from the fit. The $\chi^2$ values per number of degrees of freedom ($\chi^2$/n.d.f.) and the corresponding $p$-values are provided for each fit.

Distributions in $m_{Z'}^{T}$ for data in the SRs, together with fits of the background functions under the background-only hypothesis for the invisible channel. The number of observed events in each bin is divided by the bin width. The signal predictions are shown for different Z' boson masses, normalized to an arbitrary cross section of 1 fb. In the panels below the distributions, the ratios of data to the background function are displayed. The shaded green areas represent the statistical uncertainty from the fit. The $\chi^2$ values per number of degrees of freedom ($\chi^2$/n.d.f.) and the corresponding $p$-values are provided for each fit.

Distributions in $m_{Z'}^{T}$ for data in the SRs, together with fits of the background functions under the background-only hypothesis for the invisible channel. The number of observed events in each bin is divided by the bin width. The signal predictions are shown for different Z' boson masses, normalized to an arbitrary cross section of 1 fb. In the panels below the distributions, the ratios of data to the background function are displayed. The shaded green areas represent the statistical uncertainty from the fit. The $\chi^2$ values per number of degrees of freedom ($\chi^2$/n.d.f.) and the corresponding $p$-values are provided for each fit.

Distributions in $m_{Z'}^{rec}$ for data in the SRs, together with fits of the background functions under the background-only hypothesis for the electron channel. The number of observed events in each bin is divided by the bin width. The signal predictions are shown for different Z' boson masses, normalized to an arbitrary cross section of 1 fb. In the panels below the distributions, the ratios of data to the background function are displayed. The shaded green areas represent the statistical uncertainty from the fit. The $\chi^2$ values per number of degrees of freedom ($\chi^2$/n.d.f.) and the corresponding $p$-values are provided for each fit.

Distributions in $m_{Z'}^{rec}$ for data in the SRs, together with fits of the background functions under the background-only hypothesis for the electron channel. The number of observed events in each bin is divided by the bin width. The signal predictions are shown for different Z' boson masses, normalized to an arbitrary cross section of 1 fb. In the panels below the distributions, the ratios of data to the background function are displayed. The shaded green areas represent the statistical uncertainty from the fit. The $\chi^2$ values per number of degrees of freedom ($\chi^2$/n.d.f.) and the corresponding $p$-values are provided for each fit.

Expected upper limits at 95% CL on the product of the production cross section and the branching fraction as a function of the Z' boson mass.Each line corresponds to the three different final states and their combined result.

Expected upper limits at 95% CL on the product of the production cross section and the branching fraction as a function of the Z' boson mass.Each line corresponds to the three different final states and their combined result.

Expected and observed upper limits at 95% CL on the product of the production cross section and the branching fraction as functions of the Z' boson mass from the combination of all final states. The limits are compared with the predictions of the HVT model and the expected limits (shown by red curves) from a previous analysis.

Expected and observed upper limits at 95% CL on the product of the production cross section and the branching fraction as functions of the Z' boson mass from the combination of all final states. The limits are compared with the predictions of the HVT model and the expected limits (shown by red curves) from a previous analysis.

Prediction from the HVT model.

Prediction from the HVT model.

Observed upper limits at 95% CL on gF for different Z' boson masses as functions of the product of $g_{H}$ with the sign of $g_{F}$. The two benchmark scenarios of the HVT model are shown by the black markers.

Observed upper limits at 95% CL on gF for different Z' boson masses as functions of the product of $g_{H}$ with the sign of $g_{F}$. The two benchmark scenarios of the HVT model are shown by the black markers.

Distributions for data and expected signal and background contributions of the MVA score for the µ + jets channel in the 3j1b category, before the maximum likelihood fit. The vertical error bars represent the statistical uncertainty in the data, and the shaded band the uncertainty in the prediction. All uncertainties considered in the analysis are included in the uncertainty band. The lower panels show the data-to-prediction ratio. The first and last bins in each distribution include underflow and overflow events, respectively.

Observed and predicted number of events in each of the eight categories of the signal region, before the maximum likelihood fit. The vertical error bars represent the statistical uncertainty in the data, and the shaded band the uncertainty in the prediction. All uncertainties considered in the analysis are included in the uncertainty band. The lower panels show the data-to-prediction ratio.

Distributions for the e + jets final state before the maximum likelihood fit, MVA score bins for the 3j1b category and ∆R med ( j, j 0 ) bins for the other categories. The vertical error bars represent the statistical uncertainty in the data, and the shaded band the uncertainty of the prediction. All uncertainties considered in the analysis are included in the uncertainty band. The lower panels show the data-to-simulation ratio, where the simulations are adjusted according to their normalization before the fit. The first and last bins in each distribution include underflow and overflow events, respectively.

Distributions for the e + jets final state after the maximum likelihood fit, MVA score bins for the 3j1b category and ∆R med ( j, j 0 ) bins for the other categories. The vertical error bars represent the statistical uncertainty in the data, and the shaded band the uncertainty of the prediction. All uncertainties considered in the analysis are included in the uncertainty band. The lower panels show the data-to-simulation ratio, where the simulations are adjusted according to their normalization after the fit. The first and last bins in each distribution include underflow and overflow events, respectively.

Distributions for the µ + jets final state before the maximum likelihood fit, MVA score bins for the 3j1b category and ∆R med ( j, j 0 ) bins for the other categories. The vertical error bars represent the statistical uncertainty in the data, and the shaded band the uncertainty of the prediction. All uncertainties considered in the analysis are included in the uncertainty band. The lower panels show the data-to-simulation ratio, where the simulations are adjusted according to their normalization before the fit. The first and last bins in each distribution include underflow and overflow events, respectively.

Distributions for the µ + jets final state after the maximum likelihood fit, MVA score bins for the 3j1b category and ∆R med ( j, j 0 ) bins for the other categories. The vertical error bars represent the statistical uncertainty in the data, and the shaded band the uncertainty of the prediction. All uncertainties considered in the analysis are included in the uncertainty band. The lower panels show the data-to-simulation ratio, where the simulations are adjusted according to their normalization after the fit. The first and last bins in each distribution include underflow and overflow events, respectively.

Covariance matrix for the lepton+jets ML fit.

Covariance matrix for the combination ML fit.

Distributions in $S_T$ in the SR for the electron channel, after a background-only fit to the data. The signal distributions are scaled to the cross section predicted by the theory. The hatched bands show the post-fit uncertainty band, combining all sources of uncertainty. The ratio of data to the background predictions is shown in the panels below the distributions.

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 $|Q| = 2e$.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH3 at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 and ISR starting scale SpaceShower:pT0Ref = 1 GeV at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH3 and ISR starting scale SudakovCommon:pTmin=0.7 GeV at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-proton center-of-mass energy 38.8 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-proton center-of-mass energy 8000 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-nucleon center-of-mass energy 8160 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-nucleon center-of-mass energy 13000 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-proton center-of-mass energy 38.8 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-proton center-of-mass energy 8000 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-nucleon center-of-mass energy 8160 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-proton center-of-mass energy 13000 GeV versus the dilepton mass range of the process.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 38.8 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 62 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 200 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-anti-proton center-of-mass energy 1800 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-anti-proton center-of-mass energy 1960 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 2760 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 8000 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-nucleon center-of-mass energy 8160 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 13000 GeV when fitting to the CMS measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 13000 GeV when fitting to the LHCb measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 38.8 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 62 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 200 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-anti-proton center-of-mass energy 1800 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-anti-proton center-of-mass energy 1960 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 2760 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 8000 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-nucleon center-of-mass energy 8160 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 13000 GeV when fitting to the CMS measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 13000 GeV when fitting to the LHCb measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 38.8 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 62 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 200 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-anti-proton center-of-mass energy 1800 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-anti-proton center-of-mass energy 1960 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 2760 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 8000 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-nucleon center-of-mass energy 8160 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 13000 GeV when fitting to the CMS measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 13000 GeV when fitting to the LHCb measurement.

Observed profile likelihood projection on mH, for the 2e2mu final state, using the N-2D′ VXBS approach. Both statistical and systematic uncertainties have been considered.

Observed profile likelihood projection on mH, for the 2mu2e final state, using the N-2D′ VXBS approach. Both statistical and systematic uncertainties have been considered.

Result of the Higgs boson width measurement using the on-shell production only, shown as 1 − CL curve extracted using the Feldman–Cousins approach (black dots). The error bars represent the spread of the simulated experiments for each point.

Observed profile likelihood projections from the Higgs boson width fit using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Expected profile likelihood projections from the Higgs boson width fit using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Observed profile likelihood projections from the Higgs boson width fit using the on- and off-shell H → ZZ → 4l channel.

Expected (dashed) profile likelihood projections from the Higgs boson width fit using the on- and off-shell H → ZZ → 4l channel.

Observed 2D profile likelihood projection of the off-shell signal strength parameters (mu off-shell, mu off-shell) from the fit to the combined off-shell H → ZZ → 4l and 2l2nu channels. [Nature Phys. 18 (2022) 1329] The best fit value is shown by the black cross and the SM prediction by the red x. The 68% and 95%CL contours are given by the dashed and solid curves, respectively. The color scale to the right of the plot relates the quantitative values.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2016 pre-VPF, for positive muons.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2016 pre-VPF, for negative muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2016 pre-VPF, for positive muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2016 pre-VPF, for negative muons.

Difference between data and simulation in Z → 2e events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) ranges in 2016 pre-VPF.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2016 post-VPF, for positive muons.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2016 post-VPF, for negative muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2016 post-VPF, for positive muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2016 post-VPF, for negative muons.

Difference between data and simulation in Z → 2e events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) ranges in 2016 post-VPF.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2017, for positive muons.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2017, for negative muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2017, for positive muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2017, for negative muons.

Difference between data and simulation in Z → 2e events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) ranges in 2017.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2018, for positive muons.

Difference between data and simulation in Z → 2mu events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) range, in 2018, for negative muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2018, for positive muons.

Difference between data and simulation in J/Psi → 2mu events, normalized to simulation, as a function of the transverse momentum (pt) for two different pseudorapidity (|eta|) ranges, in 2018, for negative muons.

Difference between data and simulation in Z → 2e events, normalized to simulation, as a function of transverse momentum (pt) for three different pseudorapidity (|eta|) ranges in 2018.

The observed likelihood scan as a function of mH using the N-2D'_VXBS$ approach.

The expected likelihood scan as a function of mH using the N-2D'_VXBS$ approach. Expected likelihood has been obtained with the hypothesis mH = 125.38 GeV

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2e events in data in 2016 pre-VPF.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2mu events in data 2016 pre-VPF.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2e events in data in 2016 post-VPF.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2mu events in data 2016 post-VPF.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2e events in data in 2017.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2mu events in data 2017.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2e events in data in 2018.

Comparison of measured with the predicted dilepton mass resolution using the event-by-event mass uncertainty for Z → 2mu events in data 2018.

Observed profile likelihood projections from the Higgs boson width fit using the on- and off-shell H → ZZ → 4l channel with kappa Q constrained to 0.

Expected profile likelihood projections from the Higgs boson width fit using the on- and off-shell H → ZZ → 4l channel with kappa Q constrained to 0.

Observed profile likelihood projection on the Higgs boson width using on- and off-shell production. The analysis of H → ZZ → 4l, considering an unconstrained coupling strength κQ is presented.

Expected profile likelihood projection on the Higgs boson width using on- and off-shell production. The analysis of H → ZZ → 4l, considering an unconstrained coupling strength κQ is presented.

Expected profile likelihood projection on muF off-shell using on- and off-shell production for H → ZZ → 4l.

Observed profile likelihood projection on muF off-shell using on- and off-shell production for H → ZZ → 4l.

Expected profile likelihood projection on muF off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Observed profile likelihood projection on muF off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Expected profile likelihood projection on muV off-shell using on- and off-shell production for H → ZZ → 4l.

Observed profile likelihood projection on muV off-shell using on- and off-shell production for H → ZZ → 4l.

Expected profile likelihood projection on muV off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu.

Observed profile likelihood projection on muV off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Expected profile likelihood projection on mu off-shell using on- and off-shell production for H → ZZ → 4l.

Observed profile likelihood projection on mu off-shell using on- and off-shell production for H → ZZ → 4l.

Expected profile likelihood projection on mu off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

Observed profile likelihood projection on mu off-shell using using the full combination of H → ZZ → 4l with the off-shell H → ZZ → 2l2nu [Nature Phys. 18 (2022) 1329].

HF Fraction at $1.2 < |\eta| < 2.0$ Top Muon Rich Data

HF Fraction at $1.2 < |\eta| < 2.0$ Bottom Ratio

Charged Hadron $v_2$; $1.2 < |\eta| < 2.0$

Charged Hadron; $1.2 < |\eta| < 2.0$

HF -> $\mu$; $1.2 < |\eta| < 2.0$

HF -> $e$; $|\eta| < 0.35$

JPSI v2 in Au+Au collisions as a function of pT (GeV/c) for 10%--40% centrality with pT binned by [0, 2], [2, 5], and [5, 10] GeV/c.