Observed 95% CL exclusion limits on the simplified models of chargino-pair production decaying via W bosons.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino-pair production decaying via W bosons.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino-pair production decaying via W bosons.

Expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$+1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$-1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

Observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

Expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

$+1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

$-1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

Observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and h bosons.

Expected 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

$+1\sigma$ expected 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

$-1\sigma$ expected 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

Observed 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of higgsino GGM scenarios.

Observed upper limit on the signal cross section in fb for the production of $\tilde{\chi}_1^{+}\tilde{\chi}_{1}^{-}$.

The analyses used in combination for each scenario to set limits in models of the production of $\tilde{\chi}_1^{+}\tilde{\chi}_{1}^{-}$.

Observed upper limit on the signal cross section in fb for chargino--neutralino production decaying via W and Z bosons.

The analyses used in combination for each scenario to set limits in models of chargino--neutralino production decaying via W and Z bosons.

Expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$+1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$-1\sigma$ expected 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

Observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$+1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

$-1\sigma$ observed 95% CL exclusion limits on the simplified models of chargino--neutralino production decaying via W and Z bosons.

Observed upper limit on the signal cross section in fb for chargino--neutralino production decaying via W and h bosons.

The analyses used in combination for each scenario to set limits in models of chargino--neutralino production decaying via W and h bosons.

Observed upper limit on the signal cross section in fb for higgsino GGM scenarios.

The analyses used in combination for each scenario to set limits in higgsino GGM scenarios.

Signal yields for two charge scenarios considered in the analysis, as well as their associated uncertainties.

Distribution of $N_{\text{hits}}^{\text{low dE/dx}}$ in the SR and the CR for the early 2016 data set, as well as for an FCP signal at a mass of 100 GeV and different charge scenarios. The vertical bars and the shaded area correspond to the statistical uncertainty in the SR and the CR, respectively. The p-value of the fit is 6%. The two lower panels show the ratio of the number of tracks observed in the CR (upper) and SR (lower), and the fit function. The vertical bars correspond to the uncertainty from statistical sources, while the shaded area shows the systematic uncertainty in the fit due to the choice of the fitting function and the binomial fit range as explained in low dE/dx the main text. Comparing with respect to the binomial fit starting at $N_{\text{hits}}^{\text{low dE/dx}} = 2$, and not $N_{\text{hits}}^{\text{low dE/dx}} = 1$, is needed to account for the fact that early 2016 data is more strongly affected low dE/dx by instrumental effects that widen the N hits distribution.

Distribution of $N_{\text{hits}}^{\text{low dE/dx}}$ in the SR and the CR for the late 2016 data set, as well as for an FCP signal at a mass of 100 GeV and different charge scenarios. The vertical bars and the shaded area correspond to the statistical uncertainty in the SR and the CR, respectively. The p-value of the fit is 78%. The two lower panels show the ratio of the number of tracks observed in the CR (upper) and SR (lower), and the fit function. The vertical bars correspond to the uncertainty from statistical sources, while the shaded area shows the systematic uncertainty in the fit due to the choice of the fitting function and the binomial fit range as explained in low dE/dx the main text.

Distribution of $N_{\text{hits}}^{\text{low dE/dx}}$ in the SR and the CR for the 2017 data set, as well as for an FCP signal at a mass of 100 GeV and different charge scenarios. The vertical bars and the shaded area correspond to the statistical uncertainty in the SR and the CR, respectively. The p-value of the fit is 65%. The two lower panels show the ratio of the number of tracks observed in the CR (upper) and SR (lower), and the fit function. The vertical bars correspond to the uncertainty from statistical sources, while the shaded area shows the systematic uncertainty in the fit due to the choice of the fitting function and the binomial fit range as explained in low dE/dx the main text.

Distribution of $N_{\text{hits}}^{\text{low dE/dx}}$ in the SR and the CR for the 2018 data set, as well as for an FCP signal at a mass of 100 GeV and different charge scenarios. The vertical bars and the shaded area correspond to the statistical uncertainty in the SR and the CR, respectively. The p-value of the fit is 9%. The two lower panels show the ratio of the number of tracks observed in the CR (upper) and SR (lower), and the fit function. The vertical bars correspond to the uncertainty from statistical sources, while the shaded area shows the systematic uncertainty in the fit due to the choice of the fitting function and the binomial fit range as explained in low dE/dx the main text.

Exclusion region (hatched) at 95% CL in the FCP charge-mass plane for the considered signal. The expected exclusion is shown with the associated 1 and 2 standard deviations $\sigma$ bands. Signal points at charges 0.9, 0.8, 2/3, 0.5, and 1/3 e are connected by straight lines to guide the eye. This is a conservative interpolation. Previous exclusions from CMS [Phys. Rev. D 87 (2013) 092008), JHEP 07 (2013) 122] as well as OPAL [Phys. Lett. B 572 (2003) 8] are given for comparison.

Dilepton mass spectra for the high mass $W\phi($ee$)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $W\phi($ee$)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $W\phi($ee$)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $Z\phi($ee$)$ SR event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $Z\phi($ee$)$ SR event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $t\bar{t}\phi($ee$)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $t\bar{t}\phi($ee$)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $t\bar{t}\phi($ee$)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $t\bar{t}\phi($ee$)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $t\bar{t}\phi($ee$)$ SR3 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $t\bar{t}\phi($ee$)$ SR3 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $W\phi(\mu\mu)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $W\phi(\mu\mu)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $W\phi(\mu\mu)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $W\phi(\mu\mu)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $Z\phi(\mu\mu)$ SR event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $Z\phi(\mu\mu)$ SR event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $t\bar{t}\phi(\mu\mu)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $t\bar{t}\phi(\mu\mu)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $t\bar{t}\phi(\mu\mu)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $t\bar{t}\phi(\mu\mu)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the low mass $t\bar{t}\phi(\mu\mu)$ SR3 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the high mass $t\bar{t}\phi(\mu\mu)$ SR3 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $W\phi(\tau\tau)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $Z\phi(\tau\tau)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $W\phi(\tau\tau)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $Z\phi(\tau\tau)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $W\phi(\tau\tau)$ SR3 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $Z\phi(\tau\tau)$ SR3 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $t\bar{t}\phi(\tau\tau)$ SR1 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $t\bar{t}\phi(\tau\tau)$ SR2 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $t\bar{t}\phi(\tau\tau)$ SR3 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $t\bar{t}\phi(\tau\tau)$ SR4 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $t\bar{t}\phi(\tau\tau)$ SR5 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $t\bar{t}\phi(\tau\tau)$ SR6 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

Dilepton mass spectra for the $t\bar{t}\phi(\tau\tau)$ SR7 event selections for the combined 2016–2018 data set. The lower panel shows the ratio of observed events to the total expected SM background prediction (Obs/Exp), and the gray band represents the sum of statistical and systematic uncertainties in the background prediction. The rightmost bin contains the overflow events in each distribution. The expected background distributions and the uncertainties are shown after the data is fit under the background-only hypothesis. For illustration, two example signal hypotheses for the production and decay of a scalar and a pseudoscalar $\phi$ boson are shown, and their masses (in units of GeV) are indicated in the legend. The signals are normalized to the product of the cross section and branching fraction of 10 fb.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $W\phi$ signal with scalar couplings in the ee decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $W\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $W\phi$ signal with pseudoscalar couplings in the ee decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $W\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $W\phi$ signal with scalar couplings in the $\mu\mu$ decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $W\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $W\phi$ signal with pseudoscalar couplings in the $\mu\mu$ decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $W\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $W\phi$ signal with scalar couplings in the $\tau\tau$ decay scenario. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $W\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $W\phi$ signal with pseudoscalar couplings in the $\tau\tau$ decay scenario. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $W\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $Z\phi$ signal with scalar couplings in the ee decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $Z\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $Z\phi$ signal with pseudoscalar couplings in the ee decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $Z\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $Z\phi$ signal with scalar couplings in the $\mu\mu$ decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $Z\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $Z\phi$ signal with pseudoscalar couplings in the $\mu\mu$ decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $Z\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $Z\phi$ signal with scalar couplings in the $\tau\tau$ decay scenario. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $Z\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $Z\phi$ signal with pseudoscalar couplings in the $\tau\tau$ decay scenario. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $Z\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $W\phi$ signal with H-like production in the ee decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $W\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $Z\phi$ signal with H-like production in the ee decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $Z\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $W\phi$ signal with H-like production in the $\mu\mu$ decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $W\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $Z\phi$ signal with H-like production in the $\mu\mu$ decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $Z\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $W\phi$ signal with H-like production in the $\tau\tau$ decay scenario. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $W\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $Z\phi$ signal with H-like production in the $\tau\tau$ decay scenario. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $Z\phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal with scalar couplings in the ee decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal with pseudoscalar couplings in the ee decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal with scalar couplings in the $\mu\mu$ decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal with pseudoscalar couplings in the $\mu\mu$ decay scenario. The vertical gray band indicates the mass region not considered in the analysis. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal with scalar couplings in the $\tau\tau$ decay scenario. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal.

The 95% confidence level upper limits on the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal with pseudoscalar couplings in the $\tau\tau$ decay scenario. The red line is the theoretical prediction for the product of the production cross section and branching fraction of the $t\bar{t} \phi$ signal.

The 95% confidence level upper limits on $g^2_{tS}$ for the dilaton-like $t\bar{t} \phi$ signal model. Masses of the $\phi$ boson above 300 GeV are not probed for the dilaton-like signal model as the $\phi$ branching fraction into top quark-antiquark pairs becomes nonnegligible.

The 95% confidence level upper limits on $g^2_{tPS}$ for the axion-like $t\bar{t} \phi$ signal model. Masses of the $\phi$ boson above 300 GeV are not probed for the axion-like signal model as the $\phi$ branching fraction into top quark-antiquark pairs becomes nonnegligible.

The 95% confidence level upper limits on the product of $sin^2 \theta$ and branching fraction for the H-like production of X$\phi \rightarrow$ ee. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level upper limits on the product of $sin^2 \theta$ and branching fraction for the H-like production of X$\phi \rightarrow \mu\mu$. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level upper limits on $sin^2 \theta$ for the H-like production and decay of X$\phi$ signal model.

Cross section in units of pb for the W$\phi$, Z$\phi$, and $t\bar{t}\phi$ signals as a function of the $\phi$ boson mass in GeV. All cross sections are inclusive of all W, Z, $t\bar{t}$ and $\phi$ decay modes.

The 95% confidence level expected and observed upper limits on the product of $g^{2}_{tS}$ and $\bf{\it{B}}(\phi \rightarrow $ee$)$ of the $t\bar{t} \phi$ signal with scalar couplings, where $g_{tS}$ denotes the coupling of the $\phi$ boson to the top quark and $\bf{\it{B}}(\phi \rightarrow $ee$)$ is the branching fraction of the $\phi$ boson into an electron pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $g^{2}_{tS}$ and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ of the $t\bar{t} \phi$ signal with scalar couplings, where $g_{tS}$ denotes the coupling of the $\phi$ boson to the top quark and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ is the branching fraction of the $\phi$ boson into a muon pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $g^{2}_{tS}$ and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ of the $t\bar{t} \phi$ signal with scalar couplings, where $g_{tS}$ denotes the coupling of the $\phi$ boson to the top quark and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ is the branching fraction of the $\phi$ boson into a tau pair.

The 95% confidence level expected and observed upper limits on the product of $g^{2}_{tPS}$ and $\bf{\it{B}}(\phi \rightarrow $ee$)$ of the $t\bar{t} \phi$ signal with pseudoscalar couplings, where $g_{tPS}$ denotes the coupling of the $\phi$ boson to the top quark and $\bf{\it{B}}(\phi \rightarrow $ee$)$ is the branching fraction of the $\phi$ boson into an electron pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $g^{2}_{tPS}$ and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ of the $t\bar{t} \phi$ signal with pseudoscalar couplings, where $g_{tPS}$ denotes the coupling of the $\phi$ boson to the top quark and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ is the branching fraction of the $\phi$ boson into a muon pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $g^{2}_{tPS}$ and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ of the $t\bar{t} \phi$ signal with pseudoscalar couplings, where $g_{tPS}$ denotes the coupling of the $\phi$ boson to the top quark and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ is the branching fraction of the $\phi$ boson into a tau pair.

The 95% confidence level expected and observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow $ee$)$ of the $t\bar{t} \phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow $ee$)$ is the branching fraction of the $\phi$ boson into an electron pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ of the $t\bar{t} \phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ is the branching fraction of the $\phi$ boson into a muon pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ of the $t\bar{t} \phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ is the branching fraction of the $\phi$ boson into a tau pair.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{S}$ and $\bf{\it{B}}(\phi \rightarrow $ee$)$ of the $W\phi$ signal with scalar couplings, where $\Lambda_{S}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $ee$)$ is the branching fraction of the $\phi$ boson into an electron pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{S}$ and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ of the $W\phi$ signal with scalar couplings, where $\Lambda_{S}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ is the branching fraction of the $\phi$ boson into a muon pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{S}$ and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ of the $W\phi$ signal with scalar couplings, where $\Lambda_{S}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ is the branching fraction of the $\phi$ boson into a tau pair.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{PS}$ and $\bf{\it{B}}(\phi \rightarrow $ee$)$ of the $W\phi$ signal with pseudoscalar couplings, where $\Lambda_{PS}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $ee$)$ is the branching fraction of the $\phi$ boson into an electron pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{PS}$ and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ of the $W\phi$ signal with pseudoscalar couplings, where $\Lambda_{PS}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ is the branching fraction of the $\phi$ boson into a muon pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{PS}$ and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ of the $W\phi$ signal with pseudoscalar couplings, where $\Lambda_{PS}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ is the branching fraction of the $\phi$ boson into a tau pair.

The 95% confidence level expected and observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow $ee$)$ of the $W\phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow $ee$)$ is the branching fraction of the $\phi$ boson into an electron pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ of the $W\phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ is the branching fraction of the $\phi$ boson into a muon pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ of the $W\phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ is the branching fraction of the $\phi$ boson into a tau pair.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{S}$ and $\bf{\it{B}}(\phi \rightarrow $ee$)$ of the $Z\phi$ signal with scalar couplings, where $\Lambda_{S}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $ee$)$ is the branching fraction of the $\phi$ boson into an electron pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{S}$ and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ of the $Z\phi$ signal with scalar couplings, where $\Lambda_{S}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ is the branching fraction of the $\phi$ boson into a muon pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{S}$ and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ of the $Z\phi$ signal with scalar couplings, where $\Lambda_{S}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ is the branching fraction of the $\phi$ boson into a tau pair.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{PS}$ and $\bf{\it{B}}(\phi \rightarrow $ee$)$ of the $Z\phi$ signal with pseudoscalar couplings, where $\Lambda_{PS}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $ee$)$ is the branching fraction of the $\phi$ boson into an electron pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{PS}$ and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ of the $Z\phi$ signal with pseudoscalar couplings, where $\Lambda_{PS}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ is the branching fraction of the $\phi$ boson into a muon pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $\Lambda^{-2}_{PS}$ and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ of the $Z\phi$ signal with pseudoscalar couplings, where $\Lambda_{PS}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ is the branching fraction of the $\phi$ boson into a tau pair.

The 95% confidence level expected and observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow $ee$)$ of the $Z\phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow $ee$)$ is the branching fraction of the $\phi$ boson into an electron pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ of the $Z\phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow $$\mu\mu$$)$ is the branching fraction of the $\phi$ boson into a muon pair. The vertical gray band indicates the mass region not considered in the analysis.

The 95% confidence level expected and observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ of the $Z\phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow $$\tau\tau$$)$ is the branching fraction of the $\phi$ boson into a tau pair.

The 95% confidence level observed upper limits on the product of $\sigma$($t \bar{t} \phi$) and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $t \bar{t} \phi$ signal with scalar couplings, where $\sigma$ denotes the production cross section and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The red dash-dotted line is the theoretical prediction for $\sigma\bf{\it{B}}$ of the $t \bar{t} \phi$ signal. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\sigma$($t \bar{t} \phi$) and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $t \bar{t} \phi$ signal with pseudoscalar couplings, where $\sigma$ denotes the production cross section and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The red dash-dotted line is the theoretical prediction for $\sigma\bf{\it{B}}$ of the $t \bar{t} \phi$ signal. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\sigma$($W\phi$) and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $W\phi$ signal with scalar couplings, where $\sigma$ denotes the production cross section and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The red dash-dotted line is the theoretical prediction for $\sigma\bf{\it{B}}$ of the $W\phi$ signal. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\sigma$($W\phi$) and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $W\phi$ signal with pseudoscalar couplings, where $\sigma$ denotes the production cross section and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The red dash-dotted line is the theoretical prediction for $\sigma\bf{\it{B}}$ of the $W\phi$ signal. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\sigma$($W\phi$) and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $W\phi$ signal with H-like production, where $\sigma$ denotes the production cross section and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The red dash-dotted line is the theoretical prediction for $\sigma\bf{\it{B}}$ of the $W\phi$ signal. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\sigma$($Z\phi$) and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $Z\phi$ signal with scalar couplings, where $\sigma$ denotes the production cross section and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The red dash-dotted line is the theoretical prediction for $\sigma\bf{\it{B}}$ of the $Z\phi$ signal. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\sigma$($Z\phi$) and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $Z\phi$ signal with pseudoscalar couplings, where $\sigma$ denotes the production cross section and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The red dash-dotted line is the theoretical prediction for $\sigma\bf{\it{B}}$ of the $Z\phi$ signal. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\sigma$($Z\phi$) and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $Z\phi$ signal with H-like production, where $\sigma$ denotes the production cross section and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The red dash-dotted line is the theoretical prediction for $\sigma\bf{\it{B}}$ of the $Z\phi$ signal. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $g^{2}_{tS}$ and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $t \bar{t} \phi$ signal with scalar couplings, where $g_{tS}$ denotes the coupling of the $\phi$ boson to the top quark and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $g^{2}_{tPS}$ and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $t \bar{t} \phi$ signal with pseudoscalar couplings, where $g_{tPS}$ denotes the coupling of the $\phi$ boson to the top quark and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $t \bar{t} \phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\Lambda^{-2}_{S}$ and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $W\phi$ signal with scalar couplings, where $\Lambda_{S}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\Lambda^{-2}_{PS}$ and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $W\phi$ signal with pseudoscalar couplings, where $\Lambda_{PS}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $W\phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\Lambda^{-2}_{S}$ and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $Z\phi$ signal with scalar couplings, where $\Lambda_{S}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $\Lambda^{-2}_{PS}$ and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $Z\phi$ signal with pseudoscalar couplings, where $\Lambda_{PS}$ denotes the mass scale of the effective interaction and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The 95% confidence level observed upper limits on the product of $sin^2 \theta$ and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ for the $Z\phi$ signal with H-like production, where $\theta$ denotes the mixing angle of the Higgs boson with the $\phi$ boson and $\bf{\it{B}}(\phi \rightarrow \ell \ell)$ is the branching fraction of the $\phi$ boson into a lepton pair of given flavor. Exclusions on the dielectron, dimuon, and ditau decay scenarios of the $\phi$ boson are shown with the green, blue, and orange solid lines, respectively. The vertical gray band indicates the mass region not considered in the analysis in the dielectron and dimuon decay scenarios of the $\phi$ boson.

The product of acceptance and efficiency, $A\varepsilon$, for a scalar $\phi$ boson in the $t\bar{t} \phi$ signal (with inclusive $t\bar{t}$ decay) in each signal region in the dielectron decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a scalar $\phi$ boson in the $t\bar{t} \phi$ signal (with inclusive $t\bar{t}$ decay) in each signal region in the dimuon decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a scalar $\phi$ boson in the $t\bar{t} \phi$ signal (with inclusive $t\bar{t}$ decay) in each signal region in the ditau decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a pseudoscalar $\phi$ boson in the $t\bar{t} \phi$ signal (with inclusive $t\bar{t}$ decay) in each signal region in the dielectron decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a pseudoscalar $\phi$ boson in the $t\bar{t} \phi$ signal (with inclusive $t\bar{t}$ decay) in each signal region in the dimuon decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a pseudoscalar $\phi$ boson in the $t\bar{t} \phi$ signal (with inclusive $t\bar{t}$ decay) in each signal region in the ditau decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a scalar $\phi$ boson in the $W\phi$ signal (with leptonic $W$ decay) in each signal region in the dielectron decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a scalar $\phi$ boson in the $W\phi$ signal (with leptonic $W$ decay) in each signal region in the dimuon decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a scalar $\phi$ boson in the $W\phi$ signal (with leptonic $W$ decay) in each signal region in the ditau decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a pseudoscalar $\phi$ boson in the $W\phi$ signal (with leptonic $W$ decay) in each signal region in the dielectron decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a pseudoscalar $\phi$ boson in the $W\phi$ signal (with leptonic $W$ decay) in each signal region in the dimuon decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a pseudoscalar $\phi$ boson in the $W\phi$ signal (with leptonic $W$ decay) in each signal region in the ditau decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for an H-like $\phi$ boson in the $W\phi$ signal (with leptonic $W$ decay) in each signal region in the dielectron decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for an H-like $\phi$ boson in the $W\phi$ signal (with leptonic $W$ decay) in each signal region in the dimuon decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for an H-like $\phi$ boson in the $W\phi$ signal (with leptonic $W$ decay) in each signal region in the ditau decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a scalar $\phi$ boson in the $Z\phi$ signal (with leptonic $Z$ decay) in each signal region in the dielectron decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a scalar $\phi$ boson in the $Z\phi$ signal (with leptonic $Z$ decay) in each signal region in the dimuon decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a scalar $\phi$ boson in the $Z\phi$ signal (with leptonic $Z$ decay) in each signal region in the ditau decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a pseudoscalar $\phi$ boson in the $Z\phi$ signal (with leptonic $Z$ decay) in each signal region in the dielectron decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a pseudoscalar $\phi$ boson in the $Z\phi$ signal (with leptonic $Z$ decay) in each signal region in the dimuon decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for a pseudoscalar $\phi$ boson in the $Z\phi$ signal (with leptonic $Z$ decay) in each signal region in the ditau decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for an H-like $\phi$ boson in the $Z\phi$ signal (with leptonic $Z$ decay) in each signal region in the dielectron decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for an H-like $\phi$ boson in the $Z\phi$ signal (with leptonic $Z$ decay) in each signal region in the dimuon decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

The product of acceptance and efficiency, $A\varepsilon$, for an H-like $\phi$ boson in the $Z\phi$ signal (with leptonic $Z$ decay) in each signal region in the ditau decay scenario. Each value is computed as the ratio of the number of simulated signal events passing all selection criteria to the total number of simulated signal events, and includes the data-to-simulation correction factors described in the paper.

Selected signal shapes of the $W\phi$(ee) signal for illustration purposes. All shape parametrizations for all coupling scenarios of the $X\phi$(ee) signal are provided in the SignalShapes_XPhiToEleEle.root file, and a README file with instructions is provided under Additional Resources.

Selected signal shapes of the $W\phi$$(\mu\mu)$ signal for illustration purposes. All shape parametrizations for all coupling scenarios of the $X\phi$$(\mu\mu)$ signal are provided in the SignalShapes_XPhiToMuMu.root file, and a README file with instructions is provided under Additional Resources.

Selected signal shapes of the $W\phi$$(\tau\tau)$ signal for illustration purposes. All shape parametrizations for all coupling scenarios of the $X\phi$$(\tau\tau)$ signal are provided in the SignalShapes_XPhiToTauTau.root file, and a README file with instructions is provided under Additional Resources.

Observed exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$ and a $-1 \sigma$ deviation of the NNLO+NNLL theoretical cross-section of a $\tilde{t}_1$ pair-production.

Expected exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

The $1\sigma$ variation of expected 95% CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

The $2\sigma$ variation of expected 95%CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Observed exclusion limits at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Observed exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{ GeV}$ and a $+1 \sigma$ deviation of the NNLO+NNLL theoretical cross-section of a $\tilde{t}_1$ pair-production.

Observed exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{ GeV}$ and a $-1 \sigma$ deviation of the NNLO+NNLL theoretical cross-section of a $\tilde{t}_1$ pair-production.

Expected exclusion limits at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

The $1\sigma$ variation of expected 95% CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

The $2\sigma$ variation of expected 95%CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Observed upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$

Expected upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$

Observed upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Expected upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Post-fit distribution of $N_{\mathrm{tops}}^{\mathrm{DNN}} (\mathrm{R=1.0})$ in the SRA signal region presented without the associated SRA applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{T2}}(j^{b}_{R=1.0}, c)$ in the SRA signal region presented without the associated SRA applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $p_{\mathrm{T}}(c_{1})$ in the SRB signal region presented without the associated SRB applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{T}}(j, \mathrm{E}_{\mathrm{T}}^{\mathrm{miss}})_{\mathrm{close}}$ in the SRB signal region presented without the associated SRB applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $\mathrm{E}_{\mathrm{T}}^{\mathrm{miss}} \textrm{Significance}$ in the SRC signal region presented without the associated SRC applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{T}}(j, \mathrm{E}_{\mathrm{T}}^{\mathrm{miss}})_{\mathrm{close}}$ in the SRC signal region presented without the associated SRC applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of NN signal score in the SRD signal region presented without the associated SRD applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{eff}}$ in the SRD signal region. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Pull plots showing the SRA, SRB and SRC post-fit data and SM agreement using the background-only fit configuration

Pull plots showing the SRD post-fit data and SM agreement using the background-only fit configuration

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region A. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region B. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region C. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 750. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1000. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1250. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1500. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1750. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 2000. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Signal acceptance in the SRA$^{[450,575]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRA$^{[450,575]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRA$^{\geq 575}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRA$^{\geq 575}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRB$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRB$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRB$^{[150,400]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRB$^{[150,400]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRB$^{\geq 400}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRB$^{\geq 400}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{[150,300]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{[150,300]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{[300,500]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{[300,500]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{\geq 500}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{\geq 500}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1250 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1250 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1500 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1500 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD2000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD2000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Observed upper limits at 95% CL on B($\text{H} \rightarrow \text{a}_{1}\text{a}_{1}$) in percent, obtained from the combination of the $\mu\mu$bb and $\tau\tau$bb channels. The results are obtained as functions of $m_{\text{a}_{1}}$ for 2HDM+S Type I (independent of tan$\beta$), Type II (tan$\beta$=2.0), Type III (tan$\beta$=2.0), and Type IV (tan$\beta$=0.6), respectively.

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Central $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins, Forward $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Forward $\eta$

$\lt N_{Lund} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Central $\eta$

$\lt N_{Lund} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$, Forward $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Inclusive $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Central $\eta$

$\lt N_{Lund}^{Primary} \gt$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$, Forward $\eta$

Inclusive $\lt N_{Lund} \gt$

Inclusive $\lt N_{Lund}^{Primary} \gt$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 0.5~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 1.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 2.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 5.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 10.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 20.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $300~\text{GeV} \leq p_T < 500~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 50.0~\text{GeV}$, $1250~\text{GeV} \leq p_T < 4500~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

Data Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, All $p_T$ bins

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $500~\text{GeV} \leq p_T < 750~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $750~\text{GeV} \leq p_T < 1000~\text{GeV}$

MC Stat. Covariance, $N_{Lund}^{Primary}, k_t \geq 100.0~\text{GeV}$, $1000~\text{GeV} \leq p_T < 1250~\text{GeV}$

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E2C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

Unfolded E3C/E2C distributions in data compared to NLO+NNLL_approx predictions. Theoretial uncertainty in each bin is handled fully correlated as shape uncertainty. For the one-sided uncertainties, we symmetrize them when performing the fit.

The fitted slopes of the E3C/E2C data distributions as a function of jet pt are used to illustrate the dependency of alphas on jet pt.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

Unfolded E3C/E2C distributions in data compared to MC predictions.

The chi2 scan result using eq.3 for different alphas.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The correlation matrix is composed by 10 jet pt region, each region is represented by a block in the plot. Inside each block, there are 22 xL bins same as the E2C, E3C and E3C/E2C distributions. Therefore, the x and y bins of the correlation matrix is given by, binNumber = pT_index * 22 + xL_index.

The energy weight of E2C as defined in eq.1 in the paper is used in the unfolding, the binning is listed in this table.

The energy weight of E3C as defined in eq.1 in the paper is used in the unfolding, the binning is listed in this table.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bHbH channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bZbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bHtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 5-jet bZtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bHbH channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bZbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bHtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the hadronic 6-jet bZtW channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 3-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 3-jet bZbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 4-jet bHbZ channel.

Distributions of reconstructed VLQ mass for expected postfit background (blue histogram), signal plus background (colored lines), and observed data (black points) for events in the semileptonic 4-jet bZbZ channel.

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 0% $\mathcal{B}(B \to bH)$, 100% $\mathcal{B}(B \to bH)$, and 0% $\mathcal{B}(B \to bH)$

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 25% $\mathcal{B}(B \to bH)$, 25% $\mathcal{B}(B \to bH)$, and 50% $\mathcal{B}(B \to bH)$

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 50% $\mathcal{B}(B \to bH)$, 50% $\mathcal{B}(B \to bH)$, and 0% $\mathcal{B}(B \to bH)$

The limit at 95% CL on the cross section for VLQ pair production for the branching fraction hypothesis 100% $\mathcal{B}(B \to bH)$, 0% $\mathcal{B}(B \to bH)$, and 0% $\mathcal{B}(B \to bH)$

Median expected exclusion limits on the VLQ mass at 95% CL as a function of the branching fractions $\mathcal{B}(B \to bH)$ and $\mathcal{B}(B \to tW)$, with $\mathcal{B}(B \to tW) = 1 - \mathcal{B}(B \to bH) - \mathcal{B}(B \to bZ)$. The grey area corresponds to the region where the exclusion limit is less than 1000 GeV.

Median observed exclusion limits on the VLQ mass at 95% CL as a function of the branching fractions $\mathcal{B}(B \to bH)$ and $\mathcal{B}(B \to tW)$, with $\mathcal{B}(B \to tW) = 1 - \mathcal{B}(B \to bH) - \mathcal{B}(B \to bZ)$. The grey area corresponds to the region where the exclusion limit is less than 1000 GeV.

Overall efficiencies in the STA-STA (green) and TMS-TMS (red) dimuon categories, as well as their combination (black) as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 20\ GeV$. The solid curves show efficiencies achieved with the 2022 Run 3 triggers, whereas dashed curves show efficiencies for the subset of events selected by the triggers used in the 2018 Run 2 analysis. The efficiency is defined as the fraction of signal events that satisfy the criteria of the indicated trigger as well as the full set of offline selection criteria. The lower panel shows the relative improvement of the overall signal efficiency brought in by improvements in the trigger.

Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m_{\mu \mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the HAHM model. Yellow and green stacked histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.

Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m_{\mu \mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the HAHM model. Yellow and green stacked filled histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m^{corr}_{\mu\mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the RPV SUSY model. Yellow and green stacked histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.

Comparison of the observed (black points) and expected (histograms) numbers of events in nonoverlapping $m^{corr}_{\mu\mu}$ intervals in the STA-STA dimuon category, in the signal region optimized for the RPV SUSY model. Yellow and green stacked filled histograms represent mean expected background contributions from QCD and DY, respectively, while statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/30$, for events in all mass intervals combined. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. Notations are as in the Fig. 10 caption.

Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/30$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.

Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/4$, for events in all mass intervals combined. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. Notations are as in the Fig. 10 caption.

Distribution of min($d_0 / \sigma_{d_0}$) for TMS-TMS dimuons with $|\Delta\Phi| < \pi/4$, for events in all mass intervals combined, for both the validation (min($d_0 / \sigma_{d_0}$) < 6) and signal (min($d_0 / \sigma_{d_0}$) > 6) regions. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events. Statistical uncertainties in the total expected background are shown as hatched histograms. Signal contributions expected from simulated signals indicated in the legends are shown in red and blue. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. Events are required to satisfy all nominal selection criteria with the exception of the $d_0 / \sigma_{d_0}$ requirement. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the RPV SUSY study that requires $|\Delta\Phi| < \pi/4$, in bins of $m^{corr}_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.

Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the RPV SUSY study that requires $|\Delta\Phi| < \pi/4$, in bins of $m^{corr}_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.

Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the RPV SUSY study that requires $|\Delta\Phi| < \pi/4$, in bins of $m^{corr}_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: > 20. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow. All uncertainties shown are statistical only.

Comparison of observed and expected numbers of events in bins of $m^{corr}_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the RPV SUSY model. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m^{corr}_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Contributions expected from signal events predicted by the RPV SUSY model with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the HAHM study that requires $|\Delta\Phi| < \pi/30$, in bins of $m_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Other notations are as in the Fig. 12 caption.

Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 6-10. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the HAHM study that requires $|\Delta\Phi| < \pi/30$, in bins of $m_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Other notations are as in the Fig. 12 caption.

Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: 10-20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

Comparison of observed and expected numbers of events in the TMS-TMS dimuon category, in the HAHM study that requires $|\Delta\Phi| < \pi/30$, in bins of $m_{\mu\mu}$. The number of observed events (black circles) is overlaid with the stacked histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: > 20. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Other notations are as in the Fig. 12 caption.

Comparison of observed and expected numbers of events in bins of $m_{\mu\mu}$ in the TMS-TMS dimuon category, in the signal regions optimized for the HAHM. The number of observed events (black circles) is overlaid with the stacked filled histograms showing the expected numbers of QCD (yellow) and DY (green) background events in bins of $m_{\mu\mu}$ in min($d_0 / \sigma_{d_0}$) bin: >20. Hatched histograms show statistical uncertainties in the total expected background. Signal contributions expected from simulated $H \rightarrow Z_DZ_D$ events with the parameters indicated in the legends are shown as red and blue histograms. Their yields are set to the corresponding median expected 95% CL exclusion limits obtained from the ensemble of both dimuon categories, scaled up as indicated in the legend to improve visibility. The last bin includes events in the histogram overflow.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, in the STA-STA and TMS-TMS dimuon categories in 2022 data and their combination.The median expected limits obtained from the STA-STA and TMS-TMS dimuon categories are shown as dashed blue and red curves, respectively; the combined median expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 10\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 20\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 30\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 40\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 50\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $B(H \rightarrow Z_DZ_D)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m(Z_D) = 60\ GeV$, obtained in this analysis, the Run 2 analysis, and their combination. The observed limits in this analysis and in the Run 2 analysis are shown as blue and red curves, respectively; the median combined expected limits are shown as dashed black curves; and the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 125\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 125\ GeV$ is 7200 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 125\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 125\ GeV$ is 7200 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 200\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 200 GeV$ is 840 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 200\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 200 GeV$ is 840 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 350\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 350\ GeV$ is 50 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 350\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The predicted cross section for $m(\tilde{q}) = 350\ GeV$ is 50 pb, and falls outside the y-axis range.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 700\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 700\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1150\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1150\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1600\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

The 95% CL upper limits on $\sigma(pp \rightarrow \tilde{q}\bar{\tilde{q}})B(\tilde{q} \rightarrow q\tilde{\chi}^{0}_{1})$ as a function of $c\tau(\tilde{\chi}^{0}_{1})$ in the RPV SUSY model, for $B(\tilde{\chi}^{0}_{1} \rightarrow \mu^{+}\mu^{-}\nu) = 0.5$ and $m(\tilde{q}) = 1600\ GeV$. The observed limits for various $m(\tilde{\chi}^{0}_{1})$ indicated in the legends are shown as solid curves. The median expected limits and their 68 and 95% quantiles are shown, respectively, as dashed black curves and green and yellow bands for the case of $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$ and omitted for other neutralino masses for clarity. The gray horizontal line indicates the theoretical value of the squark-antisquark production cross section with the uncertainties shown as the gray shaded band.

Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA to TMS association procedure, as a function of generated $L_{xy}$, in all HAHM signal samples combined.

Efficiencies of the Run 2 and Run 3 displaced dimuon triggers as a function of $c\tau$ for the HAHM signal events with $m(Z_D) = 50\ GeV$. The efficiency is defined as the fraction of simulated events that satisfy the requirements of the following sets of trigger paths: the Run 2 (2018) triggers (dashed black); the Run 3 (2022, L3) triggers (blue); the Run 3 (2022, L2) triggers (red); and the OR of all these triggers (Run 3 (2022), black). The lower panel shows the ratio of the overall Run 3 (2022) efficiency to the Run 2 (2018) efficiency.

Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 1\ cm$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.

Efficiencies of the Run 2 (2018) (red) and Run 3 (2022) (black) sets of displaced dimuon triggers as a function of $m(Z_D)$ for the HAHM signal events with $c\tau = 10\ m$. The efficiency is defined as the fraction of simulated events that satisfy the detector acceptance and the requirements of the indicated set of trigger paths. The lower panel shows the ratio of the Run 3 (2022) efficiency to the Run 2 (2018) efficiency.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 20\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM signal with $m(Z_D) = 50\ GeV$ in different years of data taking. Efficiencies are computed as the ratios of the number of simulated signal events in which at least one dimuon candidate passes all 2016 (dashed green), 2018 (dashed red), and 2022 (solid black) trigger and offline selection criteria to the total number of simulated signal events. The lower panel shows the ratio of the 2022 efficiency to the 2018 efficiency (dashed red) and to the 2016 efficiency (dashed green).

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 10\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 20\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 30\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 40\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(Z_D)$ for the HAHM model with $m(Z_D) = 60\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 125\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 200\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 350\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 50\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 700\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1150\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Overall selection efficiencies as a function of $c\tau(\tilde{\chi}^{0}_{1})$ for the RPV SUSY model, for events with $m(\tilde{q}) = 1600\ GeV$ and $m(\tilde{\chi}^{0}_{1}) = 500\ GeV$. The plot shows efficiencies of the two dimuon categories, TMS-TMS (dashed red) and STA-STA (dashed green), as well as their combination (solid black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the HAHM signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ smaller than 20 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the TMS-TMS dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 20-70 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies in the STA-STA dimuon category as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true}$ 70-500 cm in the RPV SUSY signal model. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.