Showing 10 of 18 results
A search for a massive $W'$ gauge boson decaying to a top quark and a bottom quark is performed with the ATLAS detector in $pp$ collisions at the LHC. The dataset was taken at a centre-of-mass energy of $\sqrt{s} = 8$ TeV and corresponds to 20.3 fb$^{-1}$ of integrated luminosity. This analysis is done in the hadronic decay mode of the top quark, where novel jet substructure techniques are used to identify jets from high-momentum top quarks. This allows for a search for high-mass $W'$ bosons in the range $1.5 - 3.0$ TeV. $b$-tagging is used to identify jets originating from $b$-quarks. The data are consistent with Standard Model background-only expectations, and upper limits at 95% confidence level are set on the $W' \rightarrow tb$ cross section times branching ratio ranging from $0.16$ pb to $0.33$ pb for left-handed $W'$ bosons, and ranging from $0.10$ pb to $0.21$ pb for $W'$ bosons with purely right-handed couplings. Upper limits at 95% confidence level are set on the $W'$-boson coupling to $tb$ as a function of the $W'$ mass using an effective field theory approach, which is independent of details of particular models predicting a $W'$ boson.
m_tb distributions in data in the one b-tag and the two b-tag category, together with background-only fits excluding the region 4-5 TeV which is beyond the range considered for this analysis. Potential WPRIME_L signal shapes in the hadronic top-quark decay channel with gPRIME = gSM are also given for resonance masses of 1.5, 2.0, 2.5 and 3.0 TeV.
Limits at 95% CL on the cross section times branching ratio to TOP BOTTOM for the left-handed and for the right-handed WPRIME model. The expected cross section for WPRIME production with gprime = gSM is also shown.
Observed and expected 95% CL limits on the ratio of coupling gWPRIME_L/gSM (gWPRIME_R/gSM) of the WPRIME_L (WPRIME_R) model as a function of the WPRIME mass.
Selection acceptance times efficiency as a function of WPRIME mass at truth level for left- and right-handed WPRIME MC. The total efficiency curves correspond to the sum of the efficiencies of the one b-tag and two b-tag categories.
Cutflow (efficiency with respect to total number of events in %) for several WPRIME masses in the left-handed and in the right-handed model for hadronic top-quark decays.
Overview of the signal acceptance times efficiency (A x Eff) for the hadronic top-quark decay for both categories, the predicted cross section times branching ratio to TOP BOTTOM, as well as the observed 95% CL limit on the cross section times branching ratio for several WPRIME masses in the left-handed and in the right-handed model. The expected signal event yields in the two categories for 20.3 fb-1 of 8 TeV data are also shown.
A search for events with large missing transverse momentum, jets, and at least two tau leptons has been performed using 2 fb^-1 of proton-proton collision data at sqrt(s) = 7 TeV recorded with the ATLAS detector at the Large Hadron Collider. No excess above the Standard Model background expectation is observed and a 95% CL visible cross section upper limit for new phenomena is set. A 95% CL lower limit of 32 TeV is set on the GMSB breaking scale Lambda independent of tan(beta). These limits provide the most stringent tests to date in a large part of the considered parameter space.
The observed PT spectrum of the leading TAU candidates and the estimated SM background after pre-selection of candidate events, soft multi-jet rejection and the requirement of two or more TAUS and no light leptons.
The distribution of the effective mass of the two leading TAU candidates in data (with statistical uncertainties only) and the estimated SM background after pre-selection of candidate events, soft multi-jet rejection and the requirement of two or more TAUS and no light leptons.
The distribution of the sum of the transverse masses of the two leading TAU candidates in data (with statistical uncertainties only) and the estimated SM background after pre-selection of candidate events, soft multi-jet rejection and the requirement of two or more TAUS and no light leptons.
The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 0.8 to 6.8.
The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 6.8 to 12.8.
The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 12.8 to 18.8.
The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 18.8 to 24.8.
The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 24.8 to 30.8.
The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 30.8 to 36.8.
The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 36.8 to 42.8.
The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 42.8 to 48.8.
The observed 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta) over the tan(beta) region 48.8 to 51.2.
The observed and expected 95% CL limits on the minimal GMSB model parameters Lambda and tan(beta).
The acceptance, efficiency and their product in the Lambda-Tan(Beta)-plane for M_mess=250 TeV, N_5=3 and C_grav=1.
The relative systematic uncertainty from the selection, theoretical and statistical uncertainities and their combined values.
The observed 95% CL upper limits on the visible and production cross sections in the Lambda-Tan(Beta) plane for stau and slepton NLSP production in the minimal GMSB model.
Many extensions of the Standard Model posit the existence of heavy particles with long lifetimes. In this Letter, results are presented of a search for such particles, which decay at a significant distance from their production point, using a final state containing charged hadrons and an associated muon. This analysis uses a data sample of proton-proton collisions at sqrt(s)= 7 TeV corresponding to an integrated luminosity of 4.4 fb-1 collected in 2011 by the ATLAS detector operating at the Large Hadron Collider. Results are interpreted in the context of R-parity violating supersymmetric scenarios. No events in the signal region are observed and limits are set on the production cross section for supersymmetric particles, multiplied by the square of the branching ratio for a neutralino to decay to charged hadrons and a muon, as a function of the neutralino lifetime. To allow these limits to be used in a variety of models, they are presented for a range of squark and neutralino masses.
Efficiency-vs-radial-vertex-position without re-tracking The efficiency for reconstructing a displaced vertex passing all cuts, as a function of radial distance from the z-axis to the vertex positon. The retrack and noretrack suffixes refer to whether or not the procedure known as re-tracking, where the tracking algorithm is re-run with looser cuts, on the leftover hits from standard tracking, was used to select the tracks that were input to the vertexing algorithm.
Efficiency-vs-radial-vertex-position with re-tracking The efficiency for reconstructing a displaced vertex passing all cuts, as a function of radial distance from the z-axis to the vertex positon. The retrack and noretrack suffixes refer to whether or not the procedure known as re-tracking, where the tracking algorithm is re-run with looser cuts, on the leftover hits from standard tracking, was used to select the tracks that were input to the vertexing algorithm.
Event selection efficiency vs mean proper decay length The MH, HH, ML suffix used for overlaying the graphs refers to the combinations of squark and neutralino masses in the signal MC sample: MH is 700GeV squarks and 494GeV neutralinos, HH is 1.5TeV squarks and 494GeV neutralinos, and ML is 700GeV squarks and 108GeV neutralinos. The tables show the efficiency for reconstructing a signal event, where at least one vertex candidate passes all selection requirements, as a function of the proper decay length c*tau of the neutralino.
Number of tracks in vertex vs invariant mass of vertex (DATA) The number of tracks in the vertex vs the invariant mass of the vertex, for the data, and for the MH signal sample respectively. All selection cuts are applied, with the exceptions of those on the variables in the plot (Ntrk and mass), and the offline muon requirements.
Number of tracks in vertex vs invariant mass of vertex (SIGNAL) The number of tracks in the vertex vs the invariant mass of the vertex, for the data, and for the MH signal sample respectively. All selection cuts are applied, with the exceptions of those on the variables in the plot (Ntrk and mass), and the offline muon requirements.
95% CL upper limit on sigma.BR^2 vs c*tau The observed 95% CL upper limits on the cross-section for squark production, multiplied by the branching fraction for both squarks to decay to long-lived neutralinos, which in turn decay to mu+jets, given that we observed zero events in the signal region.
Expected upper limit on sigma.BR^2 vs c*tau The expected 95% CL upper limits on the cross-section for squark production, multiplied by the branching fraction for both squarks to decay to long-lived neutralinos, which in turn decay to mu+jets.
An inclusive search for long-lived exotic particles (LLPs) decaying to final states with a pair of muons is presented. The search uses data corresponding to an integrated luminosity of 36.6 fb$^{-1}$ collected by the CMS experiment from the proton-proton collisions at $\sqrt{s}$ = 13.6 TeV in 2022, the first year of Run 3 of the CERN LHC. The experimental signature is a pair of oppositely charged muons originating from a common vertex spatially separated from the proton-proton interaction point by distances ranging from several hundred $\mu$m to several meters. The sensitivity of the search benefits from new triggers for displaced dimuons developed for Run 3. The results are interpreted in the framework of the hidden Abelian Higgs model, in which the Higgs boson decays to a pair of long-lived dark photons, and of an $R$-parity violating supersymmetry model, in which long-lived neutralinos decay to a pair of muons and a neutrino. The limits set on these models are the most stringent to date in wide regions of lifetimes for LLPs with masses larger than 10 GeV.
Efficiencies of the Run 2 and Run 3 displaced dimuon triggers as a function of $c\tau$ for the HAHM signal events with $m_{Z_D} = 20\ GeV$. The efficiency is defined as the fraction of simulated events that satisfy the requirements of the following sets of trigger paths: the Run 2 (2018) triggers (dashed black); the Run 3 (2022, L3) triggers (blue); the Run 3 (2022, L2) triggers (red); and the OR of all these triggers (Run 3 (2022), black). The lower panel shows the ratio of the overall Run 3 (2022) efficiency to the Run 2 (2018) efficiency.
Efficiencies of the various displaced dimuon trigger paths and their combination as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 20\ GeV$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the following sets of trigger paths: the Run 2 (2018) triggers (dashed black); the Run 3 (2022, L3) triggers (blue); the Run 3 (2022, L2) triggers (red); and the OR of all these triggers (Run 3 (2022), black). The lower panel shows the ratio of the overall Run 3 (2022) efficiency to the Run 2 (2018) efficiency.
Efficiencies in the STA-STA (green) and TMS-TMS (red) dimuon categories, as well as their combination (black) as a function of $c\tau$ for the HAHM signal events with $m_{Z_D} = 20\ GeV$. Solid curves show efficiencies achieved with the Run 3 triggers, whereas dashed curves show efficiencies for the subset of events selected by the triggers used in the 2018 Run 2 analysis. The efficiency is defined as the fraction of signal events that satisfy the criteria of the indicated trigger as well as the full set of offline selection criteria. The lower panel shows the relative improvement of the overall signal efficiency brought in by improvements in the trigger.
Overall efficiencies in the STA-STA (green) and TMS-TMS (red) dimuon categories, as well as their combination (black) as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 20\ GeV$. The solid curves show efficiencies achieved with the 2022 Run 3 triggers, whereas dashed curves show efficiencies for the subset of events selected by the triggers used in the 2018 Run 2 analysis. The efficiency is defined as the fraction of signal events that satisfy the criteria of the indicated trigger as well as the full set of offline selection criteria. The lower panel shows the relative improvement of the overall signal efficiency brought in by improvements in the trigger.
Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m_{\mu \mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the HAHM model. Yellow and green stacked histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.
Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m_{\mu \mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the HAHM model. Yellow and green stacked filled histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m^{corr}_{\mu\mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the RPV SUSY model. Yellow and green stacked histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.
Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m^{corr}_{\mu\mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the RPV SUSY model. Yellow and green stacked filled histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/30$, for events in all mass intervals combined. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. Notations are as in the Fig. 10 caption.
Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/30$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.
Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/4$, for events in all mass intervals combined. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. Notations are as in the Fig. 10 caption.
Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/4$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the RPV SUSY study that requires $|\Delta\Phi| < \pi/4$, in bins of $m^{corr}_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.
Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the RPV SUSY study that requires $|\Delta\Phi| < \pi/4$, in bins of $m^{corr}_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.
Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the RPV SUSY study that requires $|\Delta\Phi| < \pi/4$, in bins of $m^{corr}_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: > 20. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.
Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the HAHM study that requires $|\Delta\Phi| < \pi/30$, in bins of $m_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Other notations are as in the Fig. 12 caption.
Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the HAHM study that requires $|\Delta\Phi| < \pi/30$, in bins of $m_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Other notations are as in the Fig. 12 caption.
Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the HAHM study that requires $|\Delta\Phi| < \pi/30$, in bins of $m_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: > 20. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Other notations are as in the Fig. 12 caption.
Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 125\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 125\ GeV$ is 7200 pb, and falls outside the y-axis range.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 125\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 125\ GeV$ is 7200 pb, and falls outside the y-axis range.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 200\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 200 GeV$ is 840 pb, and falls outside the y-axis range.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 200\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 200 GeV$ is 840 pb, and falls outside the y-axis range.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 350\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 350\ GeV$ is 50 pb, and falls outside the y-axis range.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 350\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 350\ GeV$ is 50 pb, and falls outside the y-axis range.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 700\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 700\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1150\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1150\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1600\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.
The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1600\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.
Efficiencies of the Run 2 and Run 3 displaced dimuon triggers as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 50\ GeV$. The efficiency is defined as the fraction of simulated events that satisfy the requirements of the following sets of trigger paths: the Run 2 (2018) triggers (dashed black); the Run 3 (2022, L3) triggers (blue); the Run 3 (2022, L2) triggers (red); and the OR of all these triggers (Run 3 (2022), black). The lower panel shows the ratio of the overall Run 3 (2022) efficiency to the Run 2 (2018) efficiency.
Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 1\ cm$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.
Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 10\ m$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 20\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 50\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 10\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 20\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 30\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 40\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 60\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 125\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 200\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 350\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
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 prediction vs. observation in the b-veto signal region
SM background observation/prediction in the bVeto signal region
SM background observation/prediction in the bVeto signal region
SM background prediction vs. observation in the WH signal region
SM background prediction vs. observation in the WH signal region
SM background observation/prediction 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 prediction vs. observation in the W signal region
SM background observation/prediction 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 prediction vs. observation in the H signal region
SM background observation/prediction in the H signal region
SM background observation/prediction in the H signal region
Observed 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
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 $\widetilde{\chi}^0_1$
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
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected 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 $\widetilde{\chi}^0_1$
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
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected 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 $\widetilde{\chi}^0_1$
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
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected 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
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected 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
Observed exclusion limits assuming the approximate-NLO+NLL cross sections
Expected 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.
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.
An inclusive search for long-lived exotic particles decaying to a pair of muons is presented. The search uses data collected by the CMS experiment at the CERN LHC in proton-proton collisions at $\sqrt{s}$ = 13 TeV in 2016 and 2018 and corresponding to an integrated luminosity of 97.6 fb$^{-1}$. The experimental signature is a pair of oppositely charged muons originating from a common secondary vertex spatially separated from the pp interaction point by distances ranging from several hundred $\mu$m to several meters. The results are interpreted in the frameworks of the hidden Abelian Higgs model, in which the Higgs boson decays to a pair of long-lived dark photons Z$_\mathrm{D}$, and of a simplified model, in which long-lived particles are produced in decays of an exotic heavy neutral scalar boson. For the hidden Abelian Higgs model with $m_\mathrm{Z_D}$ greater than 20 GeV and less than half the mass of the Higgs boson, they provide the best limits to date on the branching fraction of the Higgs boson to dark photons for $c\tau$(Z$_\mathrm{D}$) (varying with $m_\mathrm{Z_D}$) between 0.03 and ${\approx}$ 0.5 mm, and above ${\approx}$ 0.5 m. Our results also yield the best constraints on long-lived particles with masses larger than 10 GeV produced in decays of an exotic scalar boson heavier than the Higgs boson and decaying to a pair of muons.
Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2016 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 33$ GeV.
Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2016 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 33$ GeV.
Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2018 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 28$ GeV.
Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2018 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 28$ GeV.
Comparison of the number of events observed in 2016 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2016 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2018 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2018 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2016 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2016 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2018 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2018 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.
Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.
Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.
Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $c\tau(Z_D)$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.
Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $c\tau(Z_D)$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.
Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $\epsilon$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.
Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $\epsilon$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.
Background estimation and observed number of events in the STA-STA dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown. The mass interval is followed by the estimated and observed counts for the given year. The quoted uncertainties are statistical only.
Background estimations and observed numbers of events in the STA-STA dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown, followed by the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ for the given year. The quoted uncertainties are statistical only.
Background estimation and observed number of events in the TMS-TMS dimuon category in 2016 data. The mass interval is followed by the estimated and observed counts within each $min(d_0 / \sigma_{d_0})$ bin in this mass interval. The quoted uncertainties are statistical only.
Background estimations and observed numbers of events in the TMS-TMS dimuon category in 2016 data. For each mass interval, the table shows the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ in each of the three $\text{min}(d_0 / \sigma_{d_0})$ bins. The quoted uncertainties are statistical only
Background estimation and observed number of events in the TMS-TMS dimuon category in 2018 data. The mass interval is followed by the estimated and observed counts within each $min(d_0 / \sigma_{d_0})$ bin in this mass interval. The quoted uncertainties are statistical only.
Background estimations and observed numbers of events in the TMS-TMS dimuon category in 2016 data. For each mass interval, the table shows the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ in each of the three $\text{min}(d_0 / \sigma_{d_0})$ bins. The quoted uncertainties are statistical only
Background estimation and observed number of events in the STA-TMS dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown. The mass interval is followed by the estimated and observed counts for the given year. The quoted uncertainties are statistical only.
Background estimations and observed numbers of events in the STA-TMS dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown, followed by the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ for the given year. The quoted uncertainties are statistical only.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 350\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 350\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 10\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 10\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 30\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 30\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 40\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 40\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 60\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.
Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 60\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper. Efficiencies for dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the STA-TMS and TMS-TMS dimuon categories are equal to zero.
Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper. Efficiencies for dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the STA-TMS and TMS-TMS dimuon categories are equal to zero.
Searches for new phenomena in high-mass diphoton final states with the ATLAS experiment at the LHC are presented. The analysis is based on $pp$ collision data corresponding to an integrated luminosity of 36.7 fb$^{-1}$ at a centre-of-mass energy $\sqrt{s}=13$ TeV recorded in 2015 and 2016. Searches are performed for resonances with spin 0, as predicted by theories with an extended Higgs sector, and for resonances with spin 2, using a warped extra-dimension model as a benchmark model, as well as for non-resonant signals, assuming a large extra-dimension scenario. No significant deviation from the Standard Model is observed. Upper limits are placed on the production cross section times branching ratio to two photons as a function of the resonance mass. In addition, lower limits are set on the ultraviolet cutoff scale in the large extra-dimensions model.
Upper limits on the fiducial cross section times branching ratio to two photons at centre-of-mass energy of 13 TeV of a narrow-width (Γ_X = 4 MeV) spin-0 resonance as a function of its mass m_X.
Upper limits on the fiducial cross section times branching ratio to two photons at centre-of-mass energy of 13 TeV of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance equals to 2% of m_X.
Upper limits on the fiducial cross section times branching ratio to two photons at centre-of-mass energy of 13 TeV of a spin-0 resonance as a function of its mass m_X. The decay width of the resonance equals to 6% of m_X.
Upper limits on the fiducial cross section times branching ratio to two photons at centre-of-mass energy of 13 TeV of a narrow-width (Γ_X = 4 MeV) spin-0 resonance as a function of its mass m_X.
Upper limits on the production cross section times branching ratio to two photons at centre-of-mass energy of 13 TeV of the lightest KK graviton as a function of its mass m_G* for k/MPl=0.01.
Upper limits on the production cross section times branching ratio to two photons at centre-of-mass energy of 13 TeV of the lightest KK graviton as a function of its mass m_G* for k/MPl=0.10. Only the limits derived with the asymptotic formulae are quoted here.
Upper limits on the production cross section times branching ratio to two photons at centre-of-mass energy of 13 TeV of the lightest KK graviton as a function of its mass m_G* for k/MPl=0.20.
Upper limits on the production cross section times branching ratio to two photons at centre-of-mass energy of 13 TeV of the lightest KK graviton as a function of its mass m_G* for k/MPl=0.30.
Distribution of the diphoton invariant mass for data events passing the spin-0 selection. The data points are plotted at the centre of each bin. The error bars indicate statistical uncertainties. The distribution consists of not only genuine diphoton events, but also events where at least one photon is faked by a jet. There is no data event above 2700 GeV.
Distribution of the diphoton invariant mass for data events passing the spin-2 selection. The data points are plotted at the centre of each bin. The error bars indicate statistical uncertainties. The distribution consists of not only genuine diphoton events, but also events where at least one photon is faked by a jet. There is no data event above 2700 GeV.
Fiducial acceptance and product of the fiducial acceptance times the combined reconstruction and identification efficiency as a function of the mass for a spin-0 resonant signal with narrow width under the spin-0 selection. Only MC statistical uncertainties are included.
Fiducial acceptance and product of the fiducial acceptance times the combined reconstruction and identification efficiency as a function of the mass for a spin-2 resonant signal with k/MPl=0.1 under the spin-2 selection. The definition of fiducial region is provided in JHEP 09 (2016) 001. Only MC statistical uncertainties are included.
Fiducial acceptance and product of the fiducial acceptance times the combined reconstruction and identification efficiency as a function of the mass for an ADD excess (GRW formalism), calculated as the difference between the sum of ADD and SM contributions (including their interference) and the SM contribution, under the spin-2 selection in the signal region M(gamma gamma)>2240 GeV. The definition of fiducial region is provided in JHEP 09 (2016) 001, and M(gamma gamma)>2240 GeV is required in addition. Only MC statistical uncertainties are included.
A search for massive coloured resonances which are pair-produced and decay into two jets is presented. The analysis uses 36.7 fb$^{-1}$ of $\sqrt{s}=$ 13 TeV pp collision data recorded by the ATLAS experiment at the LHC in 2015 and 2016. No significant deviation from the background prediction is observed. Results are interpreted in a SUSY simplified model where the lightest supersymmetric particle is the top squark, $\tilde{t}$, which decays promptly into two quarks through $R$-parity-violating couplings. Top squarks with masses in the range 100 GeV < $m_{\tilde{t}}$ < 410 GeV are excluded at 95% confidence level. If the decay is into a $b$-quark and a light quark, a dedicated selection requiring two $b$-tags is used to exclude masses in the ranges 100 GeV < $m_{\tilde{t}}$ < 470 GeV and 480 GeV < $m_{\tilde{t}}$ < 610 GeV. Additional limits are set on the pair-production of massive colour-octet resonances.
Cutflow table for a pair produced top squark of 100 GeV decaying into a b- and an s-quark.
Cutflow table for a pair produced top squark of 500 GeV decaying into a b- and an s-quark.
Cutflow table for a pair produced coloron of 1500 GeV decaying into two quarks.
The observed number of data, background and top squark signal events in each of the signal regions of the inclusive selection
The observed number of data, background and coloron signal events in each of the signal regions of the inclusive selection
The observed number of data, background and top squark signal events in each of the signal regions of the b-tagged selection
Signal acceptance and efficiency (in %) as a function of M(STOP), before mass windows
Signal acceptance (in %) and efficiency as a function of M(STOP), after mass windows
Signal acceptance and efficiency (in %) as a function of M(RHO), before mass windows
Signal acceptance and efficiency (in %) as a function of M(RHO), after mass windows
Signal acceptance (in %) and efficiency as a function of M(STOP), before mass windows
Signal acceptance (in %) and efficiency as a function of M(STOP), after mass windows
Cross section excluded at 95% CL as a function of the top squark mass, for a pair produced top squark with decays into a pair of light-quarks.
Cross section excluded at 95% CL as a function of the cooron mass, for a pair produced coloron with decays into a pair of light-quarks.
Cross section excluded at 95% CL as a function of the top squark mass, for a pair produced top squark with decays into a b- and an s-quark.
A search is presented for particles that decay producing a large jet multiplicity and invisible particles. The event selection applies a veto on the presence of isolated electrons or muons and additional requirements on the number of b-tagged jets and the scalar sum of masses of large-radius jets. Having explored the full ATLAS 2015-2016 dataset of LHC proton-proton collisions at $\sqrt{s}=13~\mathrm{TeV}$, which corresponds to 36.1 fb$^{-1}$ of integrated luminosity, no evidence is found for physics beyond the Standard Model. The results are interpreted in the context of simplified models inspired by R-parity-conserving and R-parity-violating supersymmetry, where gluinos are pair-produced. More generic models within the phenomenological minimal supersymmetric Standard Model are also considered.
Post-fit yields for each signal region in the multijets analysis. Summary of all 27 signal regions (post-fit).
Post-fit yields for each signal region in the multijets analysis. Summary of all 27 signal regions (post-fit).
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-7j80-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-7j80-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-7j80-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-7j80-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-7j80-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-7j80-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j80-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j80-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j80-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j80-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j80-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j80-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j80-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j80-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j80-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j80-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j80-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j80-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-11j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-11j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-11j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-11j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-11j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-11j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-11j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-11j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-11j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-11j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-11j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-11j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-7j80-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-7j80-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-7j80-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-7j80-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-7j80-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-7j80-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j80-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j80-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j80-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j80-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j80-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j80-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j80-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j80-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j80-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j80-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j80-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j80-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-0b-MJ340. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-0b-MJ340. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-0b-MJ500. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-0b-MJ500. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-0b-MJ340. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-0b-MJ340. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-0b-MJ500. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-0b-MJ500. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-0b-MJ340. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-0b-MJ340. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-0b-MJ500. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-0b-MJ500. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with no b-jet requirement and a minimum transverse momentum of 50 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with no b-jet requirement and a minimum transverse momentum of 50 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with one inclusive b-jet required and a minimum transverse momentum of 50 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with one inclusive b-jet required and a minimum transverse momentum of 50 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with two inclusive b-jets required and a minimum transverse momentum of 50 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with two inclusive b-jets required and a minimum transverse momentum of 50 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with no b-jet requirement and a minimum transverse momentum of 80 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with no b-jet requirement and a minimum transverse momentum of 80 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with one inclusive b-jet required and a minimum transverse momentum of 80 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with one inclusive b-jet required and a minimum transverse momentum of 80 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with two inclusive b-jets required and a minimum transverse momentum of 80 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with two inclusive b-jets required and a minimum transverse momentum of 80 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the fat-jet stream with MJSigma above 340 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the fat-jet stream with MJSigma above 340 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the fat-jet stream with MJSigma above 500 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the fat-jet stream with MJSigma above 500 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).
95% CLs observed upper limit on model cross-section (in fb) for 2Step signal points for the best-expected signal region.
95% CLs observed upper limit on model cross-section (in fb) for 2Step signal points for the best-expected signal region.
95% CLs observed upper limit on model cross-section (in fb) for RPV signal points for the best-expected signal region.
95% CLs observed upper limit on model cross-section (in fb) for RPV signal points for the best-expected signal region.
95% CLs observed upper limit on model cross-section (in fb) for gtt off-shell signal points for the best-expected signal region.
95% CLs observed upper limit on model cross-section (in fb) for gtt off-shell signal points for the best-expected signal region.
Performance of the SR-8j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j50-0b-MJ340 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j50-0b-MJ340 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j50-0b-MJ500 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j50-0b-MJ500 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-0b-MJ340 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-0b-MJ340 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-0b-MJ500 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-0b-MJ500 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-0b-MJ340 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-0b-MJ340 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-0b-MJ500 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-0b-MJ500 for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-10j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-11j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-11j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-11j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-11j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-11j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-11j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-7j80-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-7j80-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-7j80-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-7j80-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-7j80-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-7j80-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j80-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j80-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j80-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j80-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j80-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-8j80-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j80-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j80-0b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j80-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j80-1b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j80-2b for the 2Step grid: fractional acceptance; fractional efficiency.
Performance of the SR-9j80-2b for the 2Step grid: fractional acceptance; fractional efficiency.
This paper presents a search for direct electroweak gaugino or gluino pair production with a chargino nearly mass-degenerate with a stable neutralino. It is based on an integrated luminosity of 36.1 $\mathrm{fb}^{-1}$ of $pp$ collisions at $\sqrt{s} = 13$ TeV collected by the ATLAS experiment at the LHC. The final state of interest is a disappearing track accompanied by at least one jet with high transverse momentum from initial-state radiation or by four jets from the gluino decay chain. The use of short track segments reconstructed from the innermost tracking layers significantly improves the sensitivity to short chargino lifetimes. The results are found to be consistent with Standard Model predictions. Exclusion limits are set at 95% confidence level on the mass of charginos and gluinos for different chargino lifetimes. For a pure wino with a lifetime of about 0.2 ns, chargino masses up to 460 GeV are excluded. For the strong production channel, gluino masses up to 1.65 TeV are excluded assuming a chargino mass of 460 GeV and lifetime of 0.2 ns.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the low-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the high-Emiss region.
Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the high-Emiss region.
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Model dependent upper limits on cross-section (pb) for the electroweak production are shown by grey numbers in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Model dependent upper limits on cross-section (fb) for the electroweak production are shown by grey numbers in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Model dependent upper limits on cross-section (pb) for the electroweak production are shown by grey numbers in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Model dependent upper limits on cross-section (pb) for the electroweak production are shown by grey numbers in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Model dependent upper limits on cross-section (pb) for the electroweak production are shown by grey numbers in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).
Total acceptance $\times$ efficiency of the electroweak channel. The total signal acceptance $\times$ efficiency is defined as the probability of an event passing the signal region selection when an electroweak gaugino pair is produced in a pp collision.
Total acceptance $\times$ efficiency of the electroweak channel. The total signal acceptance $\times$ efficiency is defined as the probability of an event passing the signal region selection when an electroweak gaugino pair is produced in a pp collision.
Total acceptance $\times$ efficiency of the electroweak channel. The total signal acceptance $\times$ efficiency is defined as the probability of an event passing the signal region selection when an electroweak gaugino pair is produced in a pp collision.
Total acceptance $\times$ efficiency of the electroweak channel. The total signal acceptance $\times$ efficiency is defined as the probability of an event passing the signal region selection when an electroweak gaugino pair is produced in a pp collision.
Total acceptance $\times$ efficiency of the electroweak channel. The total signal acceptance $\times$ efficiency is defined as the probability of an event passing the signal region selection when an electroweak gaugino pair is produced in a pp collision.
Total acceptance $\times$ efficiency of the strong channel. In white regions, no simulation sample is available. The left-upper triangle region is not allowed kinematically in wino-LSP scenarios. The total signal acceptance $\times$ efficiency is calculated relative to events in which the gluinos decay into electroweak gaugino pairs.
Total acceptance $\times$ efficiency of the strong channel. In white regions, no simulation sample is available. The left-upper triangle region is not allowed kinematically in wino-LSP scenarios. The total signal acceptance $\times$ efficiency is calculated relative to events in which the gluinos decay into electroweak gaugino pairs.
Total acceptance $\times$ efficiency of the strong channel. In white regions, no simulation sample is available. The left-upper triangle region is not allowed kinematically in wino-LSP scenarios. The total signal acceptance $\times$ efficiency is calculated relative to events in which the gluinos decay into electroweak gaugino pairs.
Total acceptance $\times$ efficiency of the strong channel. In white regions, no simulation sample is available. The left-upper triangle region is not allowed kinematically in wino-LSP scenarios. The total signal acceptance $\times$ efficiency is calculated relative to events in which the gluinos decay into electroweak gaugino pairs.
Total acceptance $\times$ efficiency of the strong channel. In white regions, no simulation sample is available. The left-upper triangle region is not allowed kinematically in wino-LSP scenarios. The total signal acceptance $\times$ efficiency is calculated relative to events in which the gluinos decay into electroweak gaugino pairs.
The generator-level acceptance for charginos produced in the electroweak channel as a function of the chargino $eta$ and chargino decay radius (at generator level).
The generator-level acceptance after reconstruction, for selecting and reconstructing charginos as a function of the chargino $eta$ and chargino decay radius (at generator level).
The generator-level acceptance for charginos produced in the electroweak channel as a function of the chargino $eta$ and chargino decay radius (at generator level).
The generator-level acceptance for charginos produced in the electroweak channel as a function of the chargino $eta$ and chargino decay radius (at generator level).
The generator-level acceptance for charginos produced in the electroweak channel as a function of the chargino $eta$ and chargino decay radius (at generator level).
The acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos produced in the electroweak channel as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
The generator-level acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
The acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos produced in the electroweak channel as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
The acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos produced in the electroweak channel as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
The acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos produced in the electroweak channel as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
The generator-level acceptance for charginos produced in the strong channel as a function of the chargino $\eta$ and chargino decay radius (at generator level).
The generator-level acceptance after reconstruction, for selecting and reconstructing charginos as a function of the chargino $\eta$ and chargino decay radius (at generator level).
The generator-level acceptance for charginos produced in the strong channel as a function of the chargino $\eta$ and chargino decay radius (at generator level).
The generator-level acceptance for charginos produced in the strong channel as a function of the chargino $\eta$ and chargino decay radius (at generator level).
The generator-level acceptance for charginos produced in the strong channel as a function of the chargino $\eta$ and chargino decay radius (at generator level).
The acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos produced in the strong channel as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
The generator-level acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
The acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos produced in the strong channel as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
The acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos produced in the strong channel as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
The acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos produced in the strong channel as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in direct electroweak production with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in direct electroweak production with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in direct electroweak production with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in direct electroweak production with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in direct electroweak production with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in the strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in the strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in the strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in the strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in the strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.
The event and tracklet generator-level acceptance and selection efficiency for a few electroweak signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
The event and tracklet generator-level acceptance and selection efficiency for a few electroweak signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
The event and tracklet generator-level acceptance and selection efficiency for a few electroweak signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
The event and tracklet generator-level acceptance and selection efficiency for a few electroweak signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
The event and tracklet generator-level acceptance and selection efficiency for a few electroweak signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
The event and tracklet generator-level acceptance and selection efficiency for a few strong signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
The event and tracklet generator-level acceptance and selection efficiency for a few strong signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
The event and tracklet generator-level acceptance and selection efficiency for a few strong signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
The event and tracklet generator-level acceptance and selection efficiency for a few strong signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
The event and tracklet generator-level acceptance and selection efficiency for a few strong signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.
Systematic uncertainties in the signal event yields at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. The uncertainty in the cross-section of the strong production is large due to the large effect from the PDF uncertainty.
Systematic uncertainties in the signal event yields at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. The uncertainty in the cross-section of the strong production is large due to the large effect from the PDF uncertainty.
Systematic uncertainties in the signal event yields at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. The uncertainty in the cross-section of the strong production is large due to the large effect from the PDF uncertainty.
Systematic uncertainties in the signal event yields at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. The uncertainty in the cross-section of the strong production is large due to the large effect from the PDF uncertainty.
Systematic uncertainties in the signal event yields at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. The uncertainty in the cross-section of the strong production is large due to the large effect from the PDF uncertainty.
Observed events, expected background for null signal, and expected signal yields for two benchmark models: electroweak channel with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) and strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. Also shown are the probability of a background-only experiment being more signal-like than observed ($p_0$) and the upper limit on the model-independent visible cross-section at 95\% CL. The uncertainty in the total background yield is different from the sum of uncertainties in quadrature due to anti-correlation between different backgrounds.
Observed events, expected background for null signal, and expected signal yields for two benchmark models: electroweak channel with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) and strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. Also shown are the probability of a background-only experiment being more signal-like than observed ($p_0$) and the upper limit on the model-independent visible cross-section at 95\% CL. The uncertainty in the total background yield is different from the sum of uncertainties in quadrature due to anticorrelation between different backgrounds.
Observed events, expected background for null signal, and expected signal yields for two benchmark models: electroweak channel with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) and strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. Also shown are the probability of a background-only experiment being more signal-like than observed ($p_0$) and the upper limit on the model-independent visible cross-section at 95\% CL. The uncertainty in the total background yield is different from the sum of uncertainties in quadrature due to anti-correlation between different backgrounds.
Observed events, expected background for null signal, and expected signal yields for two benchmark models: electroweak channel with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) and strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. Also shown are the probability of a background-only experiment being more signal-like than observed ($p_0$) and the upper limit on the model-independent visible cross-section at 95\% CL. The uncertainty in the total background yield is different from the sum of uncertainties in quadrature due to anti-correlation between different backgrounds.
Observed events, expected background for null signal, and expected signal yields for two benchmark models: electroweak channel with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) and strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. Also shown are the probability of a background-only experiment being more signal-like than observed ($p_0$) and the upper limit on the model-independent visible cross-section at 95\% CL. The uncertainty in the total background yield is different from the sum of uncertainties in quadrature due to anti-correlation between different backgrounds.
Effects of systematic uncertainties on the signal exclusion significance at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. Effects of uncertainties on the fake-tracklet background is smaller in the strong channel analysis because the estimated number of the fake-tracklet background events is small.
Effects of systematic uncertainties on the signal exclusion significance at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. Effects of uncertainties on the fake-tracklet background is smaller in the strong channel analysis because the estimated number of the fake-tracket background events is small.
Effects of systematic uncertainties on the signal exclusion significance at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. Effects of uncertainties on the fake-tracklet background is smaller in the strong channel analysis because the estimated number of the fake-tracklet background events is small.
Effects of systematic uncertainties on the signal exclusion significance at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. Effects of uncertainties on the fake-tracklet background is smaller in the strong channel analysis because the estimated number of the fake-tracklet background events is small.
Effects of systematic uncertainties on the signal exclusion significance at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. Effects of uncertainties on the fake-tracklet background is smaller in the strong channel analysis because the estimated number of the fake-tracklet background events is small.
Cross-section upper limits for the strong production, presented in unit of fb. Left-upper triangle region is unphysical because the wino mass is larger than the gluino mass.
Cross-section upper limits for the strong production, presented in unit of fb. Left-upper triangle region is unphysical because the wino mass is larger than the gluino mass.
Cross-section upper limits for the strong production, presented in unit of fb. Left-upper triangle region is unphysical because the wino mass is larger than the gluino mass.
Cross-section upper limits for the strong production, presented in unit of fb. Left-upper triangle region is unphysical because the wino mass is larger than the gluino mass.
Cross-section upper limits for the strong production, presented in unit of fb. Left-upper triangle region is unphysical because the wino mass is larger than the gluino mass.
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