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| CMS-SUS-19-004 ; CERN-EP-2021-015 | ||
| Search for top squarks in final states with two top quarks and several light-flavor jets in proton-proton collisions at $\sqrt{s} = $ 13 TeV | ||
| CMS Collaboration | ||
| 13 February 2021 | ||
| Phys. Rev. D 104 (2021) 032006 | ||
| Abstract: Many new physics models, including versions of supersymmetry characterized by $R$-parity violation (RPV), compressed mass spectra, long decay chains, or additional hidden sectors, predict the production of events with top quarks, low missing transverse momentum, and many additional quarks or gluons. The results of a search for new physics in events with two top quarks and additional jets are reported. The search is performed using events with at least seven jets and exactly one electron or muon. No requirement on missing transverse momentum is imposed. The study is based on a sample of proton-proton collisions at $\sqrt{s} = $ 13 TeV corresponding to 137 fb$^{-1}$ of integrated luminosity collected with the CMS detector at the LHC in 2016-2018. The data are used to determine best fit values and upper limits on the cross section for pair production of top squarks in scenarios of RPV and stealth supersymmetry. Top squark masses up to 670 (870) GeV are excluded at 95% confidence level for the RPV (stealth) scenario, and the maximum observed local significance is 2.8 standard deviations for the RPV scenario with top squark mass of 400 GeV. | ||
| Links: e-print arXiv:2102.06976 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; HepData record ; CADI line (restricted) ; | ||
| Figures & Tables | Summary | Additional Figures & Tables | References | CMS Publications |
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Additional information on efficiencies needed for reinterpretation of these results are available here A Github repository is available and details how to setup and run the neural network used for the analysis. Example input and output are also provided. |
| Figures | |
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Figure 1:
Diagrams of top squark pair production with decays to top quarks and additional light-flavor quarks for the RPV SUSY model (left) and with decays to top quarks and gluons for the stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ model (right). |
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Figure 1-a:
Diagram of top squark pair production with decays to top quarks and additional light-flavor quarks for the RPV SUSY model. |
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Figure 1-b:
Diagram of top squark pair production with decays to top quarks and gluons for the stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ model. |
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Figure 2:
The ${S_\mathrm {NN}}$ distributions for 2016 (left) and 2017+2018 (right) show the data in the SR (black points); simulated background normalized to the number of data events (filled histograms); RPV signal model with ${m_{\tilde{\mathrm{t}}}}$ of 450 GeV (red short dashed); and stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ signal model with ${m_{\tilde{\mathrm{t}}}}$ of 850 GeV (cyan long dashed). All events shown pass the SR event selection. The band on the total background histogram denotes the dominant systematic uncertainties related to the modeling of ${H_{\mathrm {T}}}$, jet mass, and jet ${p_{\mathrm {T}}}$ in the ${\mathrm{t} \mathrm{\bar{t}}}$ simulation, as well as the statistical uncertainty for the non-${\mathrm{t} \mathrm{\bar{t}}}$ components. The lower panel shows the ratio of the number of data events to the number of normalized simulated events with the band representing the difference between the nominal ratio and the ratio obtained when varying the total background by its uncertainty. |
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Figure 2-a:
The ${S_\mathrm {NN}}$ distribution for 2016 shows the data in the SR (black points); simulated background normalized to the number of data events (filled histograms); RPV signal model with ${m_{\tilde{\mathrm{t}}}}$ of 450 GeV (red short dashed); and stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ signal model with ${m_{\tilde{\mathrm{t}}}}$ of 850 GeV (cyan long dashed). All events shown pass the SR event selection. The band on the total background histogram denotes the dominant systematic uncertainties related to the modeling of ${H_{\mathrm {T}}}$, jet mass, and jet ${p_{\mathrm {T}}}$ in the ${\mathrm{t} \mathrm{\bar{t}}}$ simulation, as well as the statistical uncertainty for the non-${\mathrm{t} \mathrm{\bar{t}}}$ components. The lower panel shows the ratio of the number of data events to the number of normalized simulated events with the band representing the difference between the nominal ratio and the ratio obtained when varying the total background by its uncertainty. |
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Figure 2-b:
The ${S_\mathrm {NN}}$ distribution for 2017+2018 shows the data in the SR (black points); simulated background normalized to the number of data events (filled histograms); RPV signal model with ${m_{\tilde{\mathrm{t}}}}$ of 450 GeV (red short dashed); and stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ signal model with ${m_{\tilde{\mathrm{t}}}}$ of 850 GeV (cyan long dashed). All events shown pass the SR event selection. The band on the total background histogram denotes the dominant systematic uncertainties related to the modeling of ${H_{\mathrm {T}}}$, jet mass, and jet ${p_{\mathrm {T}}}$ in the ${\mathrm{t} \mathrm{\bar{t}}}$ simulation, as well as the statistical uncertainty for the non-${\mathrm{t} \mathrm{\bar{t}}}$ components. The lower panel shows the ratio of the number of data events to the number of normalized simulated events with the band representing the difference between the nominal ratio and the ratio obtained when varying the total background by its uncertainty. |
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Figure 3:
Distribution in ${S_\mathrm {NN}}$ of the ratio $R_M$, as defined in the text, for $ {N_\text {jets}} = $ 7 (left) and 11 (right), for the QCD CR simulation (red circles), the ${\mathrm{t} \mathrm{\bar{t}}}$ SR simulation (green squares), and data in the CR (blue crosses) for the 2016 data period. The error bars indicate the statistical uncertainty in the value of $R_M$. |
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Figure 3-a:
Distribution in ${S_\mathrm {NN}}$ of the ratio $R_M$, as defined in the text, for $ {N_\text {jets}} = $ 7, for the QCD CR simulation (red circles), the ${\mathrm{t} \mathrm{\bar{t}}}$ SR simulation (green squares), and data in the CR (blue crosses) for the 2016 data period. The error bars indicate the statistical uncertainty in the value of $R_M$. |
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Figure 3-b:
Distribution in ${S_\mathrm {NN}}$ of the ratio $R_M$, as defined in the text, for $ {N_\text {jets}} = $ 11, for the QCD CR simulation (red circles), the ${\mathrm{t} \mathrm{\bar{t}}}$ SR simulation (green squares), and data in the CR (blue crosses) for the 2016 data period. The error bars indicate the statistical uncertainty in the value of $R_M$. |
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Figure 4:
Fitted background prediction and observed data counts for 2016, 2017, 2018A, and 2018B (from upper to lower rows) as functions of ${N_\text {jets}}$ in each of the four bins in ${S_\mathrm {NN}}$. The signal distributions normalized to the predicted cross section for the RPV model with $ {m_{\tilde{\mathrm{t}}}} = $ 450 GeV and the stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ model with $ {m_{\tilde{\mathrm{t}}}} = $ 850 GeV are shown for comparison. The lower panel of each plot displays the difference between the number of observed events and the number of events determined by the fit divided by the statistical uncertainty associated with the observed number of events ($\delta $) as black points with error bars denoting $\delta $. The blue band shows the total systematic uncertainty in the fit from all nuisance parameters. |
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Figure 4-a:
Fitted background prediction and observed data counts for 2016 as functions of ${N_\text {jets}}$ in each of the four bins in ${S_\mathrm {NN}}$. The signal distributions normalized to the predicted cross section for the RPV model with $ {m_{\tilde{\mathrm{t}}}} = $ 450 GeV and the stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ model with $ {m_{\tilde{\mathrm{t}}}} = $ 850 GeV are shown for comparison. The lower panel displays the difference between the number of observed events and the number of events determined by the fit divided by the statistical uncertainty associated with the observed number of events ($\delta $) as black points with error bars denoting $\delta $. The blue band shows the total systematic uncertainty in the fit from all nuisance parameters. |
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Figure 4-b:
Fitted background prediction and observed data counts for 2017 as functions of ${N_\text {jets}}$ in each of the four bins in ${S_\mathrm {NN}}$. The signal distributions normalized to the predicted cross section for the RPV model with $ {m_{\tilde{\mathrm{t}}}} = $ 450 GeV and the stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ model with $ {m_{\tilde{\mathrm{t}}}} = $ 850 GeV are shown for comparison. The lower panel displays the difference between the number of observed events and the number of events determined by the fit divided by the statistical uncertainty associated with the observed number of events ($\delta $) as black points with error bars denoting $\delta $. The blue band shows the total systematic uncertainty in the fit from all nuisance parameters. |
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Figure 4-c:
Fitted background prediction and observed data counts for 2018A as functions of ${N_\text {jets}}$ in each of the four bins in ${S_\mathrm {NN}}$. The signal distributions normalized to the predicted cross section for the RPV model with $ {m_{\tilde{\mathrm{t}}}} = $ 450 GeV and the stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ model with $ {m_{\tilde{\mathrm{t}}}} = $ 850 GeV are shown for comparison. The lower panel displays the difference between the number of observed events and the number of events determined by the fit divided by the statistical uncertainty associated with the observed number of events ($\delta $) as black points with error bars denoting $\delta $. The blue band shows the total systematic uncertainty in the fit from all nuisance parameters. |
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Figure 4-d:
Fitted background prediction and observed data counts for 2018B as functions of ${N_\text {jets}}$ in each of the four bins in ${S_\mathrm {NN}}$. The signal distributions normalized to the predicted cross section for the RPV model with $ {m_{\tilde{\mathrm{t}}}} = $ 450 GeV and the stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ model with $ {m_{\tilde{\mathrm{t}}}} = $ 850 GeV are shown for comparison. The lower panel displays the difference between the number of observed events and the number of events determined by the fit divided by the statistical uncertainty associated with the observed number of events ($\delta $) as black points with error bars denoting $\delta $. The blue band shows the total systematic uncertainty in the fit from all nuisance parameters. |
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Figure 5:
Background prediction from the background-only fit and observed data counts as a function of ${N_\text {jets}}$ summed over data periods and ${S_\mathrm {NN}}$ bins. Overlaid are expected distributions for the RPV and stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ models with $ {m_{\tilde{\mathrm{t}}}} = $ 450 and 850 GeV, respectively, normalized according to the top squark pair production cross section. For visualization purposes, the hatched band in the lower panel shows the quadrature sum of all of the uncertainties on the background prediction. |
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Figure 6:
Expected and observed 95% CL upper limit on the top squark pair production cross section as a function of the top squark mass for the RPV (left) and stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ (right) SUSY models. Particle masses and branching fractions assumed for each model are included on each plot. The expected cross section computed at NNLO+NNLL accuracy is shown in the red curve. |
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Figure 6-a:
Expected and observed 95% CL upper limit on the top squark pair production cross section as a function of the top squark mass for the RPV SUSY model. Particle masses and branching fractions assumed for the model are included. The expected cross section computed at NNLO+NNLL accuracy is shown in the red curve. |
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Figure 6-b:
Expected and observed 95% CL upper limit on the top squark pair production cross section as a function of the top squark mass for the stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ SUSY model. Particle masses and branching fractions assumed for the model are included. The expected cross section computed at NNLO+NNLL accuracy is shown in the red curve. |
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Figure 7:
Local $p$-value as a function of top squark mass for the RPV (left) and stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ models (right). The colored lines show the $p$-values for separate fits of the 2016 (red dash dotted), 2017 (blue dotted), 2018A (green short dashed), and 2018B (orange long dashed) data sets; the black line shows the $p$-value for the simultaneous fit of data sets. The lower panels show the best fit signal strength ($\sigma _\text {meas.}/\sigma _\text {pred.}$) as a function of top squark mass with uncertainty denoted by the green band. |
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Figure 7-a:
Local $p$-value as a function of top squark mass for the RPV model. The colored lines show the $p$-values for separate fits of the 2016 (red dash dotted), 2017 (blue dotted), 2018A (green short dashed), and 2018B (orange long dashed) data sets; the black line shows the $p$-value for the simultaneous fit of data sets. The lower panel shows the best fit signal strength ($\sigma _\text {meas.}/\sigma _\text {pred.}$) as a function of top squark mass with uncertainty denoted by the green band. |
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Figure 7-b:
Local $p$-value as a function of top squark mass for the stealth $\mathrm{S}\mathrm{Y}\overline{\mathrm{Y}}$ model. The colored lines show the $p$-values for separate fits of the 2016 (red dash dotted), 2017 (blue dotted), 2018A (green short dashed), and 2018B (orange long dashed) data sets; the black line shows the $p$-value for the simultaneous fit of data sets. The lower panel shows the best fit signal strength ($\sigma _\text {meas.}/\sigma _\text {pred.}$) as a function of top squark mass with uncertainty denoted by the green band. |
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Figure 8:
The upper panel shows the fit values ($\theta $) and uncertainties ($\delta _\theta $) for a selection of nuisance parameters (NP) from both the background-only fit (purple) and signal+background fit (blue) for the RPV model with $ {m_{\tilde{\mathrm{t}}}} = $ 400 GeV. The x-axis labels refer to the NP sources described in Section 5, the data period (16, 17, etc.), and the direction of variation ($+$,$-$). The lower panel shows the $\Delta \chi ^{2}\equiv \chi ^{2}(\mathrm {s}+\mathrm {b})-\chi ^{2}(\mathrm {b})$ difference of $\chi ^2\equiv (\theta /\delta _\theta)^2$ from the signal+background (s+b) and background-only (b) fits as a red point for each NP and the cumulative sum of $\Delta \chi ^2$ from left to right as a blue shaded histogram. The fourteen selected NP are those with $| \Delta \chi ^2| > 0.3$, and the NP are ordered from left to right by decreasing $| \Delta \chi ^2| $. The rightmost bin, separated by a vertical solid line, shows the sum of $\Delta \chi ^2$ for all NP not displayed in the figure (red point) and the sum of $\Delta \chi ^2$ for all NP (blue shaded histogram). |
| Tables | |
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Table 1:
Summary of the impact of systematic uncertainties in the expected event yields for the ${\mathrm{t} \mathrm{\bar{t}}}$ background, minor backgrounds (both ${{\mathrm{t} \mathrm{\bar{t}}}}$+X and other), and the RPV signal model with $ {m_{\tilde{\mathrm{t}}}} = $ 550 GeV. Abbreviated names for each source of uncertainty are explained in the text. For sources of uncertainty for which the size of the impact depends on ${N_\text {jets}}$ and ${S_\mathrm {NN}}$, a representative range of values showing the 16$^{\mathrm {th}}$ and 84$^{\mathrm {th}}$ percentile of all the corrections is listed with the maximum value from all bins shown in parentheses. All values are in units of percent. |
| Summary |
|
A first of its kind search for top squark pair production with subsequent decay characterized by two top quarks, additional gluons or light-flavor quarks, and low missing transverse momentum (${p_{\mathrm{T}}}^{\text{miss}}$) is described. Events containing exactly one electron or muon and at least seven jets, of which at least one should be b tagged, are selected from a sample of proton-proton collisions at $\sqrt{s} = $ 13 TeV corresponding to an integrated luminosity of 137 fb$^{-1}$ collected with the CMS detector in 2016-2018. No requirement is made on ${p_{\mathrm{T}}}^{\text{miss}}$. The dominant $\mathrm{t\bar{t}}$ background is predicted from data using a simultaneous fit of the jet multiplicity distribution across four bins of a neural network score. The results are interpreted in terms of top squark pair production in the context of $R$-parity violating (RPV) and stealth supersymmetry models. Top squark masses (${m_{\tilde{\mathrm{t}}}} $) up to 670 GeV are excluded at 95% confidence level for the RPV model in which the top squark decays to a top quark and the lightest neutralino, which subsequently decays to three light-flavor quarks via an off-shell squark through a trilinear coupling ${\lambda}''$. Top squark masses up to 870 GeV are excluded for the stealth supersymmetry model in which the top squark decays to a top quark, three gluons, and a gravitino via intermediate hidden sector particles. The maximum observed local significance is 2.8 standard deviations corresponding to a best fit signal strength of 0.21 $\pm$ 0.07 for the RPV model with ${m_{\tilde{\mathrm{t}}}} = $ 400 GeV. |
| Additional Figures | |
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Additional Figure 1:
Shown are the chosen bin edges for the four $S_{\textrm {NN}}$ bins (vertical) as a function of $N_{\textrm {jets}}$ (horizontal) for the four data taking periods. Edges are chosen (per-data taking period) by maximizing a simple significance-like metric with the constraint that the fraction of $\mathrm{t\bar{t}}$ in a given $S_{\textrm {NN}}$ bin is the same for each $N_{\textrm {jets}}$. $S_{\textrm {NN},1}$ is shown in dark blue, $S_{\textrm {NN},2}$ in light blue, $S_{\textrm {NN},3}$ in light green, and $S_{\textrm {NN},4}$ in dark green. |
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Additional Figure 5-2:
Shown is the $\chi ^2$ distribution for all nuisance parameters in the fit. This is done for the combined fit to data assuming the RPV 400 stop mass. Background-only (blue) and signal+background (purple) results are displayed. |
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Additional Figure 2-a:
The $S_{\textrm {NN}}$ distribution for $\mathrm{t\bar{t}}$ events grouped by the number of jets in the event. Shown is the result when not using gradient reversal in the NN to remove dependence on $N_{\textrm {jets}}$. |
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Additional Figure 2-b:
The $S_{\textrm {NN}}$ distribution for $\mathrm{t\bar{t}}$ events grouped by the number of jets in the event. Shown is the result when gradient reversal is employed, resulting in a much more similar $S_{\textrm {NN}}$ for $\mathrm{t\bar{t}}$ events, regardless of the number of jets in the event. |
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Additional Figure 3:
Shown are the ROC curves for the 2016 (left) and 2017 (right) trainings of the NN. In different colors are the result for a specific stop mass for the RPV model. Note that the NN was trained on all signal masses. |
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Additional Figure 3-a:
Shown are the ROC curves for the 2016 training of the NN. In different colors are the result for a specific stop mass for the RPV model. Note that the NN was trained on all signal masses. |
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Additional Figure 3-b:
Shown are the ROC curves for the 2017 training of the NN. In different colors are the result for a specific stop mass for the RPV model. Note that the NN was trained on all signal masses. |
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Additional Figure 4:
Shown, for 2016, 2017, 2018A, 2018B, is the value of the nuisance parameters when doing the background-only (purple) and signal+background (blue) fit assuming the RPV 400 signal. Then in red is the $\Delta \chi ^{2}$ for each nuisance parameter when going from background-only to signal+background. NPs are sorted by magnitude of $\Delta \chi ^{2}$. |
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Additional Figure 4-a:
Shown, for 2016, is the value of the nuisance parameters when doing the background-only (purple) and signal+background (blue) fit assuming the RPV 400 signal. Then in red is the $\Delta \chi ^{2}$ for each nuisance parameter when going from background-only to signal+background. NPs are sorted by magnitude of $\Delta \chi ^{2}$. |
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Additional Figure 4-b:
Shown, for 2017, is the value of the nuisance parameters when doing the background-only (purple) and signal+background (blue) fit assuming the RPV 400 signal. Then in red is the $\Delta \chi ^{2}$ for each nuisance parameter when going from background-only to signal+background. NPs are sorted by magnitude of $\Delta \chi ^{2}$. |
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Additional Figure 4-c:
Shown, for 2018A, is the value of the nuisance parameters when doing the background-only (purple) and signal+background (blue) fit assuming the RPV 400 signal. Then in red is the $\Delta \chi ^{2}$ for each nuisance parameter when going from background-only to signal+background. NPs are sorted by magnitude of $\Delta \chi ^{2}$. |
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Additional Figure 4-d:
Shown, for 2018B, is the value of the nuisance parameters when doing the background-only (purple) and signal+background (blue) fit assuming the RPV 400 signal. Then in red is the $\Delta \chi ^{2}$ for each nuisance parameter when going from background-only to signal+background. NPs are sorted by magnitude of $\Delta \chi ^{2}$. |
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Additional Figure 5:
Shown is the $\chi ^2$ distribution for all nuisance parameters in the fit. This is done for the individual year and combined fit to data assuming the RPV 400 stop mass. Background-only (blue) and signal+background (purple) results are displayed. |
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Additional Figure 5-a:
Shown is the $\chi ^2$ distribution for all nuisance parameters in the fit. This is done for the 2016 fit to data assuming the RPV 400 stop mass. Background-only (blue) and signal+background (purple) results are displayed. |
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Additional Figure 5-b:
Shown is the $\chi ^2$ distribution for all nuisance parameters in the fit. This is done for the 2017 fit to data assuming the RPV 400 stop mass. Background-only (blue) and signal+background (purple) results are displayed. |
png pdf |
Additional Figure 5-c:
Shown is the $\chi ^2$ distribution for all nuisance parameters in the fit. This is done for the 2018A combined fit to data assuming the RPV 400 stop mass. Background-only (blue) and signal+background (purple) results are displayed. |
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Additional Figure 5-d:
Shown is the $\chi ^2$ distribution for all nuisance parameters in the fit. This is done for the 2018B combined fit to data assuming the RPV 400 stop mass. Background-only (blue) and signal+background (purple) results are displayed. |
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Additional Figure 5-e:
Shown is the $\chi ^2$ distribution for all nuisance parameters in the fit. This is done for the individual year and combined fit to data assuming the RPV 400 stop mass. Background-only (blue) and signal+background (purple) results are displayed. |
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Additional Figure 6:
Shown is the $\Delta \chi ^2$ distribution for all nuisance parameters in the fit. This is done for the individual year and combined fit to data assuming the RPV 400 signal hypothesis. The difference is between the NP $\chi $ value found with the signal+background fit and that found for the background only fit. |
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Additional Figure 6-a:
Shown is the $\Delta \chi ^2$ distribution for all nuisance parameters in the fit. This is done for the 2016 fit to data assuming the RPV 400 signal hypothesis. The difference is between the NP $\chi $ value found with the signal+background fit and that found for the background only fit. |
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Additional Figure 6-b:
Shown is the $\Delta \chi ^2$ distribution for all nuisance parameters in the fit. This is done for the 2017 fit to data assuming the RPV 400 signal hypothesis. The difference is between the NP $\chi $ value found with the signal+background fit and that found for the background only fit. |
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Additional Figure 6-c:
Shown is the $\Delta \chi ^2$ distribution for all nuisance parameters in the fit. This is done for the 2018A fit to data assuming the RPV 400 signal hypothesis. The difference is between the NP $\chi $ value found with the signal+background fit and that found for the background only fit. |
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Additional Figure 6-d:
Shown is the $\Delta \chi ^2$ distribution for all nuisance parameters in the fit. This is done for the 2018B fit to data assuming the RPV 400 signal hypothesis. The difference is between the NP $\chi $ value found with the signal+background fit and that found for the background only fit. |
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Additional Figure 6-e:
Shown is the $\Delta \chi ^2$ distribution for all nuisance parameters in the fit. This is done for the combined fit to data assuming the RPV 400 signal hypothesis. The difference is between the NP $\chi $ value found with the signal+background fit and that found for the background only fit. |
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Additional Figure 7:
Shown are some of the input variables to the NN (2016). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 7-a:
Shown is an input variable to the NN (2016). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 7-b:
Shown is an input variable to the NN (2016). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 7-c:
Shown is an input variable to the NN (2016). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 7-d:
Shown is an input variable to the NN (2016). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 7-e:
Shown is an input variable to the NN (2016). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 7-f:
Shown is an input variable to the NN (2016). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 7-g:
Shown is an input variable to the NN (2016). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 7-h:
Shown is an input variable to the NN (2016). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 8:
Shown are some of the input variables to the NN (2017). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
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Additional Figure 8-a:
Shown is an input variable to the NN (2017). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
png pdf |
Additional Figure 8-b:
Shown is an input variable to the NN (2017). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
png pdf |
Additional Figure 8-c:
Shown is an input variable to the NN (2017). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
png pdf |
Additional Figure 8-d:
Shown is an input variable to the NN (2017). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
png pdf |
Additional Figure 8-e:
Shown is an input variable to the NN (2017). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
png pdf |
Additional Figure 8-f:
Shown is an input variable to the NN (2017). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
png pdf |
Additional Figure 8-g:
Shown is an input variable to the NN (2017). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
png pdf |
Additional Figure 8-h:
Shown is an input variable to the NN (2017). A comparison is made between $\mathrm{t\bar{t}}$ (blue) and two signal model/mass hypotheses. |
png pdf |
Additional Figure 9:
Shown is a comparison of data and simulation (2016) for the same selection of input variables as shown in Fig. 7. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
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Additional Figure 9-a:
Shown is a comparison of data and simulation (2016) for the same selection of input variables as shown in Fig. 7. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
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Additional Figure 9-b:
Shown is a comparison of data and simulation (2016) for the same selection of input variables as shown in Fig. 7. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
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Additional Figure 9-c:
Shown is a comparison of data and simulation (2016) for the same selection of input variables as shown in Fig. 7. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
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Additional Figure 9-d:
Shown is a comparison of data and simulation (2016) for the same selection of input variables as shown in Fig. 7. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
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Additional Figure 9-e:
Shown is a comparison of data and simulation (2016) for the same selection of input variables as shown in Fig. 7. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 9-f:
Shown is a comparison of data and simulation (2016) for the same selection of input variables as shown in Fig. 7. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 9-g:
Shown is a comparison of data and simulation (2016) for the same selection of input variables as shown in Fig. 7. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 9-h:
Shown is a comparison of data and simulation (2016) for the same selection of input variables as shown in Fig. 7. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
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Additional Figure 10:
Shown is a comparison of data and simulation (2017) for the same selection of input variables as shown in Fig. 8. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
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Additional Figure 10-a:
Shown is a comparison of data and simulation (2017) for the same selection of input variables as shown in Fig. 8. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 10-b:
Shown is a comparison of data and simulation (2017) for the same selection of input variables as shown in Fig. 8. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 10-c:
Shown is a comparison of data and simulation (2017) for the same selection of input variables as shown in Fig. 8. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 10-d:
Shown is a comparison of data and simulation (2017) for the same selection of input variables as shown in Fig. 8. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 10-e:
Shown is a comparison of data and simulation (2017) for the same selection of input variables as shown in Fig. 8. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 10-f:
Shown is a comparison of data and simulation (2017) for the same selection of input variables as shown in Fig. 8. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 10-g:
Shown is a comparison of data and simulation (2017) for the same selection of input variables as shown in Fig. 8. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 10-h:
Shown is a comparison of data and simulation (2017) for the same selection of input variables as shown in Fig. 8. Simulation has been normalized to data. The uncertainty band in the ratio panel includes several of the main systematic uncertainties (added in quadrature). |
png pdf |
Additional Figure 11:
Projected 95% CL upper limits on the top squark pair production cross section as a function of the top squark mass for the RPV (left) and stealth (right) SUSY models with the 3000 fb$^{-1}$ expected at the HL-LHC. Systematic uncertainties from Run 2 are reduced based on the expected improvements detailed in Refs. [85,86]. Accordingly, uncertainties associated with modeling are reduced by 50%. Those related to available statistics are scaled down by the square root of the luminosity ratio. Experimental uncertainties are reduced according to the prescribed recommendations. Uncertainties with negligible impacts either remain unchanged from Run 2 or are removed entirely. Observed limits with the Run 2 dataset of 137 fb$^{-1}$ are also overlaid. Assumed particle masses and branching fractions are included on the plot. The expected cross section computed at NNLO+NNLL accuracy is shown in the red curve. Relative to Run 2, the expected cross-section sensitivity is projected to improve by a factor of 3-7. Exclusion limits on the top squark mass are projected to reach approximately 870 GeV (1190 GeV) in the RPV (stealth) SUSY scenario, compared to 670 GeV (870 GeV) for Run 2. |
png pdf |
Additional Figure 11-a:
Projected 95% CL upper limits on the top squark pair production cross section as a function of the top squark mass for the RPV model with the 3000 fb$^{-1}$ expected at the HL-LHC. Systematic uncertainties from Run 2 are reduced based on the expected improvements detailed in Refs. [85,86]. Accordingly, uncertainties associated with modeling are reduced by 50%. Those related to available statistics are scaled down by the square root of the luminosity ratio. Experimental uncertainties are reduced according to the prescribed recommendations. Uncertainties with negligible impacts either remain unchanged from Run 2 or are removed entirely. Observed limits with the Run 2 dataset of 137 fb$^{-1}$ are also overlaid. Assumed particle masses and branching fractions are included on the plot. The expected cross section computed at NNLO+NNLL accuracy is shown in the red curve. Relative to Run 2, the expected cross-section sensitivity is projected to improve by a factor of 3-7. Exclusion limits on the top squark mass are projected to reach approximately 870 GeV in the RPV scenario, compared to 670 GeV for Run 2. |
png pdf |
Additional Figure 11-b:
Projected 95% CL upper limits on the top squark pair production cross section as a function of the top squark mass for the stealth SUSY model with the 3000 fb$^{-1}$ expected at the HL-LHC. Systematic uncertainties from Run 2 are reduced based on the expected improvements detailed in Refs. [85,86]. Accordingly, uncertainties associated with modeling are reduced by 50%. Those related to available statistics are scaled down by the square root of the luminosity ratio. Experimental uncertainties are reduced according to the prescribed recommendations. Uncertainties with negligible impacts either remain unchanged from Run 2 or are removed entirely. Observed limits with the Run 2 dataset of 137 fb$^{-1}$ are also overlaid. Assumed particle masses and branching fractions are included on the plot. The expected cross section computed at NNLO+NNLL accuracy is shown in the red curve. Relative to Run 2, the expected cross-section sensitivity is projected to improve by a factor of 3-7. Exclusion limits on the top squark mass are projected to reach approximately 1190 GeV in the stealth SUSY scenario, compared to 870 GeV for Run 2. |
| Additional Tables | |
png pdf |
Additional Table 1:
Cut flow of the two signals shown in Fig. 4a (2016). $M(\ell,\mathrm{b})$ refers to the invariant mass of the system formed by the b-tagged jet and the lepton. At each stage of the cut flow all event weights used for the signal region are applied. |
png pdf |
Additional Table 2:
Cut flow of the two signals shown in Fig. 4b (2017). $M(\ell,\mathrm{b})$ refers to the invariant mass of the system formed by the b-tagged jet and the lepton. At each stage of the cut flow all event weights used for the signal region are applied. |
png pdf |
Additional Table 3:
Cut flow of the two signals shown in Fig. 4c (2018A). $M(\ell,\mathrm{b})$ refers to the invariant mass of the system formed by the b-tagged jet and the lepton. At each stage of the cut flow all event weights used for the signal region are applied. |
png pdf |
Additional Table 4:
Cut flow of the two signals shown in Fig. 4d (2018B). $M(\ell,\mathrm{b})$ refers to the invariant mass of the system formed by the b-tagged jet and the lepton. At each stage of the cut flow all event weights used for the signal region are applied. |
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