Correlation for the uncertainties of the differential cross-section of prompt inclusive production of long-lived charged particles.

Correlation for the uncertainties of the differential cross-section of prompt inclusive production of long-lived charged particles.

Correlation for the uncertainties of the differential cross-section of prompt inclusive production of long-lived charged particles.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Differential signal yields for various signal hypotheses.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Background and data yields in the control and signal region bins. The prediction before ("prefit") and after the background only fit ("b-only") are given separately.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Matrix of covariance coefficients between signal region bins. The coefficients are obtained from the background-only fit to the control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Background prediction and observed data yields in the signal region bins. The background yields are obtained from the background-only fit to the corresponding control regions, and serve as input to the simplified likelihood reinterpretation scheme.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Unweighted signal acceptance times efficiency at every cut stage. The requirements called "HCAL mitigation" refer to the requirements imposed in the 2018 data set in order to mitigate the localized failure of the HCAL detector.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with axial couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the simplified model with vector couplings.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a axial mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{\chi}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Upper limits on the coupling $g_{q}$ in the simplified model with a vector mediator.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with scalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the signal strength in the simplified model with pseudoscalar couplings.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Exclusion limits on the fundamental Planck scale $M_{D}$ as a function of the number of extra dimensions $d$.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Median Expected exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Observed exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected plus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Expected minus 1 s.d. exclusion contour in the $m_{med}$-$m_{\chi}$ plane in the fermion portal model.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.

Tagging efficiency for AK8 jets. The efficiency includes the effect of the machine-learning based DeepAK8 tagger, as well as the application of the mass window requirement on the jet. The efficiency is split depending on the matching generator-level object. For reinterpretation purposes, this efficiency can directly be applied to any AK8 jet that is matched to a given type of generator-level object, with no prior selection on the jet mass. Other acceptance requirements on jet $\p_{T}$ and $\eta$ should still be applied.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.400\pm0.056~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.597\pm0.057~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.793\pm0.057~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 0.992\pm0.058~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 1.189\pm0.058~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 1.435\pm0.086~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

This table contains thinned out samples of the Markov chains used in the parameter estimation of the SDME measurements for $-(t-t_\text{0}) = 1.761\pm0.111~\text{GeV}^2/c^2$, reported in the main article. One in about 250 steps in the chain, which results in 200 different sets of SDMEs, is provided. These values should be used instead of bootstrapping of the results, in order to estimate uncertainties of physics models fitted to this data. To assess how the uncertainties propagate to the model uncertainties, one should evaluate the model under scrutiny for each of the 200 different sets of SDMEs. Plotting all resulting lines in a single plot will create bands which reflect the influence of the uncertainties in the data on the model. This method has the great advantage that all correlations are accurately taken into account.

The correlation matrix for the Higgs pT measurements, both for the unregularized and regularized fits. The last bin is inclusive.

The correlation matrix for the jet multiplicity measurements, both for the unregularized and regularized fits. The last bin is inclusive.

The correlation matrix for the leading jet pT measurements, both for the unregularized and regularized fits. The last bin is inclusive.

The fiducial integrated signal strength and cross section.

${p}_{T}^{miss}$ distribution of data and MC events in the signal region with the signal stacked on top of the background prediction for a mass hypothesis of ${m}_{stop} = 225GeV$ and ${m}_{LSP} = 50 GeV$. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio. The uncertainty band includes statistical, background normalization and all systematic uncertainties.

${p}_{T}^{miss}$ normalized distribution for signal points with different top squark and neutralino masses and SM ttbar events are compared.

${m}_{T2} (e\mu)$ normalized distribution for signal points with different top squark and neutralino masses and SM ttbar events are compared.

$\Delta\eta (e\mu)$ normalized distribution for signal points with different top squark and neutralino masses and SM ttbar events are compared.

$\Delta\phi (e\mu)$ normalized distribution for signal points with different top squark and neutralino masses and SM ttbar events are compared.

Normalized DNN score distribution in the signal region for two mass hypotheses to compare signal and ttbar background: $m_{LSP}=$ 50 (100) GeV and $m_{stop}=$ 225 (275) GeV.

Post-fit DNN score distributions in the signal region with the signal superimposed and scaled by a factor of 20 for different mass hypotheses of $({m}_{stop}, {m}_{LSP})=$ (225, 50) GeV. The uncertainty band includes statistical, background normalization, and all systematic uncertainties. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio before the fit (line) and after (dots), each of them with their corresponding band of uncertainties, and the ratio between the sum of the signal and background predictions and the background prediction. The masses of the signal model correspond to the values of the DNN mass parameters in each distribution.

Post-fit DNN score distributions in the signal region with the signal superimposed and scaled by a factor of 20 for different mass hypotheses of $({m}_{stop}, {m}_{LSP})=$ (275, 100) GeV. The uncertainty band includes statistical, background normalization, and all systematic uncertainties. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio before the fit (line) and after (dots), each of them with their corresponding band of uncertainties, and the ratio between the sum of the signal and background predictions and the background prediction. The masses of the signal model correspond to the values of the DNN mass parameters in each distribution.

Post-fit DNN score distributions in the signal region with the signal superimposed and scaled by a factor of 20 for different mass hypotheses of $({m}_{stop}, {m}_{LSP})=$ (275, 70) GeV. The uncertainty band includes statistical, background normalization, and all systematic uncertainties. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio before the fit (line) and after (dots), each of them with their corresponding band of uncertainties, and the ratio between the sum of the signal and background predictions and the background prediction. The masses of the signal model correspond to the values of the DNN mass parameters in each distribution.

Post-fit DNN score distributions in the signal region with the signal superimposed and scaled by a factor of 20 for different mass hypotheses of $({m}_{stop}, {m}_{LSP})=$ (245, 100) GeV. The uncertainty band includes statistical, background normalization, and all systematic uncertainties. Events from ttW, ttZ, DY, non-prompt leptons, and diboson processes are grouped into the 'Other' category. The lower panel contains the data-to-prediction ratio before the fit (line) and after (dots), each of them with their corresponding band of uncertainties, and the ratio between the sum of the signal and background predictions and the background prediction. The masses of the signal model correspond to the values of the DNN mass parameters in each distribution.

Upper limit at 95% CL on the signal cross section as a function of the top squark and neutralino masses. The model is excluded for all of the colored region inside the black boundary.

Upper limit at 95% CL on the signal cross section as a function of the top squark mass for mass difference of 175 GeV. The green and yellow bands represent the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The purple dotted line indicates the approximate NNLO + NNLL productioncross section.

Upper limit at 95% CL on the signal cross section as a function of the top squark mass for mass difference of 185 GeV. The green and yellow bands represent the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The purple dotted line indicates the approximate NNLO + NNLL productioncross section.

Upper limit at 95% CL on the signal cross section as a function of the top squark mass for mass difference of 165 GeV. The green and yellow bands represent the regions containing 68 and 95%, respectively, of the distribution of limits expected under the background-only hypothesis. The purple dotted line indicates the approximate NNLO + NNLL productioncross section.

Summary of the contributions of the experimental uncertainties in the DNN score distribution for signal and the ttbar background. The values represent the relative variation in the number of expected events across different signal models in the signal region.

Summary of the contribution of each modeling uncertainty source to the DNN score distribution for the ttbar background.

95% CL expected upper limit on top squark cross section for the direct decay model.

95% CL observed upper limit on top squark cross section for the direct decay model.

Contour of the region excluded with 95% CL (expected).

Contour of the region excluded with 95% CL (observed).

95% CL expected upper limit on top squark cross section for the cascade decay model.

95% CL observed upper limit on top squark cross section for the cascade decay model.

Contour of the region excluded with 95% CL (expected).

Contour of the region excluded with 95% CL (observed).

95% CL expected upper limit on top squark cross section for the mixed decay model.

95% CL observed upper limit on top squark cross section for the mixed decay model.

Contour of the region excluded with 95% CL (expected).

Contour of the region excluded with 95% CL (observed).

95% CL expected limit on $\sigma/\sigma_{theory}$ for a fermionic DM particle with m = 1 GeV, as a function of the mediator mass for a scalar.

95% CL observed limit on $\sigma/\sigma_{theory}$ for a fermionic DM particle with m = 1 GeV, as a function of the mediator mass for a scalar.

95% CL expected limit on $\sigma/\sigma_{theory}$ for a fermionic DM particle with m = 1 GeV, as a function of the mediator mass for a pseudoscalar.

95% CL observed limit on $\sigma/\sigma_{theory}$ for a fermionic DM particle with m = 1 GeV, as a function of the mediator mass for a pseudoscalar.

Differential cross section as a function of $m_{\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $p_{T,\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $a_{T,\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $\phi_{\eta}*$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $\pi-\Delta\phi_{\gamma\gamma}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

Differential cross section as a function of $|cos\theta*|^{(CS)}$. The table contains the values measured in data and theory predictions from SHERPA, DIPHOX and NNLOJET.

$v_3$ vs $p_T$, p+Au at 200 GeV, 0-5% central, BBCS-FVTXS-CNT detector combination

$v_3$ vs $p_T$, d+Au at 200 GeV, 0-5% central, BBCS-FVTXS-CNT detector combination

$v_3$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, BBCS-FVTXS-CNT detector combination

$v_2$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_2$ vs $p_T$, d+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_2$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_2$ ratio vs $p_T$, p/d/3He+Au at 200 GeV, 0-5% central

$v_3$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_3$ vs $p_T$, d+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$v_3$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, FVTXS-CNT-FVTXN detector combination

$c_2$ vs $\eta$, p+Au at 200 GeV, 0-5% central

$c_2$ vs $\eta$, d+Au at 200 GeV, 0-5% central

$c_2$ vs $\eta$, 3He+Au at 200 GeV, 0-5% central

$c_3$ vs $\eta$, p+Au at 200 GeV, 0-5% central

$c_3$ vs $\eta$, d+Au at 200 GeV, 0-5% central

$c_3$ vs $\eta$, 3He+Au at 200 GeV, 0-5% central

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, BBCS-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, BBCS-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, BBCS-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, BBCS-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, BBCS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, BBCS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, BBCS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, BBCS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, FVTXS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, FVTXS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, FVTXS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, FVTXS-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, CNT-BBCS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, CNT-BBCS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, CNT-BBCS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, CNT-BBCS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, CNT-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, CNT-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, CNT-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, CNT-FVTXS correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+p at 200 GeV, minimum bias, CNT-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, p+Au at 200 GeV, 0-5% central, CNT-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, d+Au at 200 GeV, 0-5% central, CNT-FVTXN correlation

$C(\Delta\phi)$ vs $\Delta\phi$, 3He+Au at 200 GeV, 0-5% central, CNT-FVTXN correlation

$c_n$ vs detector combination, p+p at 200 GeV, Minimum Bias

$c_n$ vs $p_T$, p+p at 200 GeV, Minimum Bias, BBCS-CNT detector combination

$c_n$ vs $p_T$, p+p at 200 GeV, Minimum Bias, FVTXS-CNT detector combination

$c_n$ vs $p_T$, p+p at 200 GeV, Minimum Bias, FVTXN-CNT detector combination

$c_n$ vs detector combination, p+Au at 200 GeV, 0-5% central

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, BBCS-CNT detector combination

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXS-CNT detector combination

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXN-CNT detector combination

$c_n$ vs detector combination, p+Au at 200 GeV, 0-5% central

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, BBCS-CNT detector combination

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXS-CNT detector combination

$c_n$ vs $p_T$, p+Au at 200 GeV, 0-5% central, FVTXN-CNT detector combination

$c_n$ vs detector combination, p+Au at 200 GeV, 0-5% central

$c_n$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, BBCS-CNT detector combination

$c_n$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, FVTXS-CNT detector combination

$c_n$ vs $p_T$, 3He+Au at 200 GeV, 0-5% central, FVTXN-CNT detector combination

95% CL exclusion limits on $\mathcal{B}(H\rightarrow aa \rightarrow b\bar{b}b\bar{b})$ for $m_a = 45$ GeV.

95% CL exclusion limits on $\mathcal{B}(H\rightarrow aa \rightarrow b\bar{b}b\bar{b})$ for $m_a = 55$ GeV.

The fraction of $a$ boson decays matched to reconstructed displaced vertices passing all vertex selections in signal MC.

The extrapolated signal selection efficiency as a function of $c\tau_{a}$.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow d\bar{d}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 7 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 7 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow \tau^{+} \tau^{-}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 15 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 40 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The 95% CL observed and expected limits on the branching fraction B(H $\rightarrow$ SS) for 55 GeV mass and $ S \rightarrow b\bar{b}$ decay mode.

The cluster efficiency in bins of hadronic and EM energy in region A. Region A is defined as 391 cm $< r <$ 695.5 cm and 400 cm $< |z| <$ 671 cm. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The cluster efficiency in bins of hadronic and EM energy in region A. Region A is defined as 391 cm $< r <$ 695.5 cm and 400 cm $< |z| <$ 671 cm. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The cluster efficiency in bins of hadronic and EM energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The cluster efficiency in bins of hadronic and EM energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bins correspond to LLPs that decayed leptonically with 0 hadronic energy. The cluster efficiency includes all cluster-level selections described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 35% and 20% for region A and B, respectively.

The efficiency of $N_{station} > 1$ requirement in bins of hadronic energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bin corresponds to LLPs that decayed leptonically with 0 hadronic energy. The efficiency is calculated with respect to clusters that pass all cluster-level cuts described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 10%.

The efficiency of $N_{station} > 1$ requirement in bins of hadronic energy in region B. Region B is defined as 671 cm $< |z| <$ 1100 cm, $r <$ 695.5 cm, and $|\eta| <$ 2. The cluster efficiency is estimated with LLPs decaying to $\tau^{+} \tau^{-}$. The sample contains equal fractions of events with LLP mass of 7, 15, 40, and 55 GeV and LLP lifetime of 0.1, 1, 10, and 100m. The first hadronic energy bin corresponds to LLPs that decayed leptonically with 0 hadronic energy. The efficiency is calculated with respect to clusters that pass all cluster-level cuts described in the paper, except for the jet veto, time cut, and $\Delta\phi$ cut. The full simulation signal yield prediction for samples with various LLP mass between 7 - 55 GeV, lifetime between 0.1 - 100 m, and decay mode to $d\bar{d}$ and $\tau^{+} \tau^{-}$ can be reproduced using this parameterization to within 10%.

The geometric signal acceptance as a function of $c\tau$. The acceptance is shown for four different LLP mass hypotheses: 7, 15, 40, and 55 GeV. The acceptance is defined by requiring at least 1 LLP to decay in the region defined as 400 cm $< |z| <$ 1100 cm, $r < $ 695.5 cm, and $|\eta| <$ 2.4.