CMSPASSUS23001  
Search for top squarks in final states with many light flavor jets and 0, 1, or 2 leptons in protonproton collisions at $ \sqrt{s}= $ 13 TeV  
CMS Collaboration  
6 June 2024  
Abstract: Several new physics models including versions of supersymmetry (SUSY) characterized by $ R $parity violation (RPV) or with 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 top squarks decaying to two top quarks and six additional lightflavor quarks or gluons are reported. The search employs a novel machine learning method for datadriven background estimation using decorrelated discriminators referred to as the ABCDisCoTEC technique. The search is performed using events with 0, 1, or 2 electrons or muons in conjunction with at least six jets. No requirement is placed on the presence of missing transverse momentum. The result is based on a sample of protonproton collisions at $ \sqrt{s} = $ 13 TeV corresponding to 138 fb$ ^{1} $ of integrated luminosity collected with the CMS detector at the LHC in 20162018. The data are used to determine upper limits on the top squark pair production cross section in the frameworks of RPV and stealth SUSY. Models with top squark masses less than 700 (920) GeV are excluded at the 95% confidence level for RPV (stealth) SUSY scenarios.  
Links: CDS record (PDF) ; Physics Briefing ; CADI line (restricted) ; 
Figures  
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Figure 1:
Diagrams of top squark pair production with decays to top quarks and additional lightflavor 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 1a:
Diagrams of top squark pair production with decays to top quarks and additional lightflavor 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 1b:
Diagrams of top squark pair production with decays to top quarks and additional lightflavor 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 2:
For the NN training for the RPV signal model and the 1$ \ell $ channel, 1D distributions of $ S_{\mathrm{NN},1} $ (upper left) and $ S_{\mathrm{NN},2} $ (upper right) for data (black solid line), prefit simulated SM backgrounds (stacked filled histograms) and two RPV signal models with $ m_{\tilde{\mathrm{t}}}= $ 400 and 800 GeV (dashed lines), as well as the 2D distribution of $ S_{\mathrm{NN},2} $ versus $ S_{\mathrm{NN},1} $ (lower) visualized using a Gaussian kernel density estimation (KDE) for the $ m_{\tilde{\mathrm{t}}} $ = 800 GeV RPV signal model (red) and simulated $ {\mathrm{t}\overline{\mathrm{t}}} {+}$jets background events (gray). The corresponding ABCD bin boundaries for this signal model and channel are also shown in the dashed vertical and horizontal lines. 
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Figure 2a:
For the NN training for the RPV signal model and the 1$ \ell $ channel, 1D distributions of $ S_{\mathrm{NN},1} $ (upper left) and $ S_{\mathrm{NN},2} $ (upper right) for data (black solid line), prefit simulated SM backgrounds (stacked filled histograms) and two RPV signal models with $ m_{\tilde{\mathrm{t}}}= $ 400 and 800 GeV (dashed lines), as well as the 2D distribution of $ S_{\mathrm{NN},2} $ versus $ S_{\mathrm{NN},1} $ (lower) visualized using a Gaussian kernel density estimation (KDE) for the $ m_{\tilde{\mathrm{t}}} $ = 800 GeV RPV signal model (red) and simulated $ {\mathrm{t}\overline{\mathrm{t}}} {+}$jets background events (gray). The corresponding ABCD bin boundaries for this signal model and channel are also shown in the dashed vertical and horizontal lines. 
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Figure 2b:
For the NN training for the RPV signal model and the 1$ \ell $ channel, 1D distributions of $ S_{\mathrm{NN},1} $ (upper left) and $ S_{\mathrm{NN},2} $ (upper right) for data (black solid line), prefit simulated SM backgrounds (stacked filled histograms) and two RPV signal models with $ m_{\tilde{\mathrm{t}}}= $ 400 and 800 GeV (dashed lines), as well as the 2D distribution of $ S_{\mathrm{NN},2} $ versus $ S_{\mathrm{NN},1} $ (lower) visualized using a Gaussian kernel density estimation (KDE) for the $ m_{\tilde{\mathrm{t}}} $ = 800 GeV RPV signal model (red) and simulated $ {\mathrm{t}\overline{\mathrm{t}}} {+}$jets background events (gray). The corresponding ABCD bin boundaries for this signal model and channel are also shown in the dashed vertical and horizontal lines. 
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Figure 2c:
For the NN training for the RPV signal model and the 1$ \ell $ channel, 1D distributions of $ S_{\mathrm{NN},1} $ (upper left) and $ S_{\mathrm{NN},2} $ (upper right) for data (black solid line), prefit simulated SM backgrounds (stacked filled histograms) and two RPV signal models with $ m_{\tilde{\mathrm{t}}}= $ 400 and 800 GeV (dashed lines), as well as the 2D distribution of $ S_{\mathrm{NN},2} $ versus $ S_{\mathrm{NN},1} $ (lower) visualized using a Gaussian kernel density estimation (KDE) for the $ m_{\tilde{\mathrm{t}}} $ = 800 GeV RPV signal model (red) and simulated $ {\mathrm{t}\overline{\mathrm{t}}} {+}$jets background events (gray). The corresponding ABCD bin boundaries for this signal model and channel are also shown in the dashed vertical and horizontal lines. 
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Figure 3:
A comparison of the simulationbased closure correction $ \kappa $ (blue circle), nonclosure $ {C}\hspace{0.70em}/\kern 0.0em \ $ in data (solid purple triangle), and FSR systematic uncertainty (red circle) is shown for each $ N_\text{jets} $ category for the RPV model for the 0$ \ell $ (upper), 1$ \ell $ (middle), and 2$ \ell $ (lower panel) channels. The value of $ {C}\hspace{0.70em}/\kern 0.0em \ $ shown is the maximum value of the stepping procedure described in the text. The nonclosure in the lowest $ N_\text{jets} $ bin is equivalent to the databased nonclosure systematic for each of the channels. Open purple triangles show the statistical uncertainty on $ {C}\hspace{0.70em}/\kern 0.0em \ $ in data for categories for which all VR and ABCD boundaries have a signal fraction above 5%. All databased nonclosure values are calculated after applying the simulationbased closure correction, such that an observed nonclosure of zero signifies identical modeling of the $ S_{\mathrm{NN},1} $$ S_{\mathrm{NN},2} $ correlation in simulation and data. 
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Figure 4:
$ N_\text{jets} $ distributions from the backgroundonly fits to data. Fits are run with the signal strength fixed to zero and all background process event yields are obtained from their postfit values. The signal distribution overlaid is for the RPV model with $ m_{\tilde{\mathrm{t}}} = $ 400 GeV. The three plots shown are for the 0$ \ell $, 1$ \ell $, and 2$ \ell $ channels (top to bottom) and the four panels in each plot show the $ N_\text{jets} $ distributions for the $ \mathbf{A} $, $ \mathbf{B} $, $ \mathbf{C} $, and $ \mathbf{D} $ regions (left to right). The grey error band shows the combined statistical and systematic uncertainty from the backgroundonly postfit distributions. 
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Figure 4a:
$ N_\text{jets} $ distributions from the backgroundonly fits to data. Fits are run with the signal strength fixed to zero and all background process event yields are obtained from their postfit values. The signal distribution overlaid is for the RPV model with $ m_{\tilde{\mathrm{t}}} = $ 400 GeV. The three plots shown are for the 0$ \ell $, 1$ \ell $, and 2$ \ell $ channels (top to bottom) and the four panels in each plot show the $ N_\text{jets} $ distributions for the $ \mathbf{A} $, $ \mathbf{B} $, $ \mathbf{C} $, and $ \mathbf{D} $ regions (left to right). The grey error band shows the combined statistical and systematic uncertainty from the backgroundonly postfit distributions. 
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Figure 4b:
$ N_\text{jets} $ distributions from the backgroundonly fits to data. Fits are run with the signal strength fixed to zero and all background process event yields are obtained from their postfit values. The signal distribution overlaid is for the RPV model with $ m_{\tilde{\mathrm{t}}} = $ 400 GeV. The three plots shown are for the 0$ \ell $, 1$ \ell $, and 2$ \ell $ channels (top to bottom) and the four panels in each plot show the $ N_\text{jets} $ distributions for the $ \mathbf{A} $, $ \mathbf{B} $, $ \mathbf{C} $, and $ \mathbf{D} $ regions (left to right). The grey error band shows the combined statistical and systematic uncertainty from the backgroundonly postfit distributions. 
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Figure 4c:
$ N_\text{jets} $ distributions from the backgroundonly fits to data. Fits are run with the signal strength fixed to zero and all background process event yields are obtained from their postfit values. The signal distribution overlaid is for the RPV model with $ m_{\tilde{\mathrm{t}}} = $ 400 GeV. The three plots shown are for the 0$ \ell $, 1$ \ell $, and 2$ \ell $ channels (top to bottom) and the four panels in each plot show the $ N_\text{jets} $ distributions for the $ \mathbf{A} $, $ \mathbf{B} $, $ \mathbf{C} $, and $ \mathbf{D} $ regions (left to right). The grey error band shows the combined statistical and systematic uncertainty from the backgroundonly postfit distributions. 
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Figure 5:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (left) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (right) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the 0$ \ell $ channel (upper), 1$ \ell $ channel (middle), and 2$ \ell $ channel (lower). The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
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Figure 5a:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (left) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (right) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the 0$ \ell $ channel (upper), 1$ \ell $ channel (middle), and 2$ \ell $ channel (lower). The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
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Figure 5b:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (left) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (right) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the 0$ \ell $ channel (upper), 1$ \ell $ channel (middle), and 2$ \ell $ channel (lower). The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
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Figure 5c:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (left) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (right) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the 0$ \ell $ channel (upper), 1$ \ell $ channel (middle), and 2$ \ell $ channel (lower). The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
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Figure 5d:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (left) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (right) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the 0$ \ell $ channel (upper), 1$ \ell $ channel (middle), and 2$ \ell $ channel (lower). The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
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Figure 5e:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (left) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (right) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the 0$ \ell $ channel (upper), 1$ \ell $ channel (middle), and 2$ \ell $ channel (lower). The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
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Figure 5f:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (left) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (right) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the 0$ \ell $ channel (upper), 1$ \ell $ channel (middle), and 2$ \ell $ channel (lower). The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
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Figure 6:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (upper) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (lower) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the combination of all three channels. The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
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Figure 6a:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (upper) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (lower) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the combination of all three channels. The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
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Figure 6b:
95% CL upper limit on $ \sigma_{\tilde{\mathrm{t}}\bar{\tilde{\mathrm{t}}}} $ for the RPV (upper) and stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ (lower) SUSY signal models as a function of $ m_{\tilde{\mathrm{t}}} $ for the combination of all three channels. The median expected limit is shown in the dashed blue line, with the 68% and 95% intervals shown in lightblue and yellow, respectively. The observed limit is shown in black. The vertical dashed line denotes the transition from the lowmass optimization to highmass optimization ABCD boundaries. Additionally, the theoretical cross section is shown in red with its uncertainty in light brown. 
Tables  
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Table 1:
The list of the search region selections for the 0$ \ell $, 1$ \ell $, and 2$ \ell $ channels. $ N_{\text{leptons}}^{\text{iso}} $ and $ N_{\text{muon}}^{\text{noniso}} $ denote the number of isolated leptons and nonisolated muons, respectively. 
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Table 2:
Lower bounds on $ (S_{\mathrm{NN},1},S_{\mathrm{NN},2}) $ defining the $ \mathbf{A} $ region for the low and highmass optimization for each signal model and search channel. 
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Table 3:
Magnitude of systematic uncertainties for the 0$ \ell $, 1$ \ell $, and 2$ \ell $ channels based on the RPVtrained NN and ABCD bin boundaries optimized for low $ m_{\tilde{\mathrm{t}}} $. Reported values are in units of % and ranges correspond to the 16th and 84th percentile for the value of a systematic uncertainty across all applicable analysis regions (ABCD regions and $ N_\text{jets} $ categories). The maximum value for a given systematic uncertainty across these regions is shown in parentheses. Other backgrounds include the QCD multijet and minor background processes. The RPV signal model used has $ m_{\tilde{\mathrm{t}}} = $ 550 GeV. The systematic values based on the RPVtrained model are representative of the values obtained for the $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $trained model. 
Summary 
A search for the pair production of top squarks with decays to top quarks and six additional gluons or lightflavor quarks via stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ and RPV SUSY decays is presented. The search is performed using protonproton collision events at $ \sqrt{s} = $ 13 TeV, corresponding to an integrated luminosity of 138 fb$ ^{1} $, collected by the CMS detector between 2016 and 2018. Events are selected in three search channels, defined as having at least six jets and zero, one, or two leptons. No requirement is placed on the presence of $ p_{\mathrm{T}}^\text{miss} $. This analysis is an extension of a previous search for these signatures [7], which observed a deviation with local significance of 2.8 standard deviations for a top squark mass of 400 GeV for the RPV model. The main improvements of this search are the addition of the zero and twolepton channels as well as the inclusion of a novel, neuralnetworkbased background estimation method referred to as ABCDisCoTEC. The key feature of this new method is the creation of two uncorrelated neural network variables that can be used with an ABCDstyle background estimation method. The backgrounds are estimated from a simultaneous binned likelihood fit to all search channels in several categories of jet multiplicity, with the contribution from the main $ {\mathrm{t}\overline{\mathrm{t}}} {+}$jets background constrained via the ABCD relationship that encodes the independence between the two neural network variables. This new background estimation method reduced the dependence of the analysis on uncertainties related to the modeling of the jet multiplicity spectrum compared to Ref. [7]. Overall, good agreement between the data and the background prediction is observed, and the deviation observed previously is not confirmed. The results are interpreted using two top squark decay topologies which generate low$ p_{\mathrm{T}}^\text{miss} $ signatures: the stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ and Rparity violating models. Top squark masses up to 700 GeV for the RPV model with top squark decays to a top quark and neutralino with subsequent decay of the neutralino to three lightflavored quarks are excluded at 95% confidence level. An upper mass exclusion of 930 GeV is placed on the stealth $ \mathrm{S} \mathrm{Y} \overline{\mathrm{Y}} $ model where top squarks decay via a top quark, gluons, and a soft gravitino via a hidden sector. 
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Compact Muon Solenoid LHC, CERN 