A summary of the uncertainties in the background estimates for SR-Gtt-0L-M1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-0L-M2. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-0L-M2. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-0L-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-0L-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1L-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1L-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1L-M1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1L-M1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1L-M2. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1L-M2. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1L-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1L-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-M. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-M. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtb-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtb-B. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtb-M. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtb-M. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtb-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtb-C. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-2100-1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-2100-1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1800-1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1800-1. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-2300-1200. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-2300-1200. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1900-1400. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gtt-1900-1400. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-2800-1400. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-2800-1400. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-2300-1000. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-2300-1000. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-2100-1600. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-2100-1600. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-2000-1800. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

A summary of the uncertainties in the background estimates for SR-Gbb-2000-1800. The individual experimental and theoretical uncertainties are assumed to be uncorrelated and are combined by adding in quadrature.

Results of the background-only fit extrapolated to SR_Gtt_0L_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_0L_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_0L_M1 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_0L_M1 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_0L_M2 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_0L_M2 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_0L_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_0L_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1L_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1L_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1L_M1 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1L_M1 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1L_M2 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1L_M2 in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1L_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1L_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_M in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_M in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtb_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtb_B in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtb_M in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtb_M in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtb_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtb_C in the CC analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_2100_1 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_2100_1 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1800_1 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1800_1 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_2300_1200 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_2300_1200 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1900_1400 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gtt_1900_1400 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_2800_1400 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_2800_1400 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_2300_1000 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_2300_1000 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_2100_1600 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_2100_1600 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_2000_1800 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Results of the background-only fit extrapolated to SR_Gbb_2000_1800 in the NN analysis, for both the total expected background yields and the main contributing background processes. The quoted uncertainties include both experimental and theoretical systematics. The data in the SRs are not included in the fit. The background category $t\bar{t}+X$ includes $t\bar{t} W/Z$, $t\bar{t} H$ and $t\bar{t} t\bar{t}$ events. The row ``Pre-fit background'' provides the total background prediction when the $t\bar{t}$ and $Z+$jets normalisations are obtained from theoretical calculation, taking into account the kinematic weights described in Section 5.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the NN analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~GeV, obtained from the CC analysis.

Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~GeV, obtained from the CC analysis.

Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~GeV, obtained from the CC analysis.

Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~GeV, obtained from the CC analysis.

Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 600$~GeV, obtained from the CC analysis.

Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 600$~GeV, obtained from the CC analysis.

Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 600$~GeV, obtained from the CC analysis.

Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 600$~GeV, obtained from the CC analysis.

Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~TeV, obtained from the CC analysis.

Observed (left) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~TeV, obtained from the CC analysis.

Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~TeV, obtained from the CC analysis.

Expected (right) 95\%~CL exclusion limits on the gluino mass as a function of BR$(\tilde{g} \to b\bar{b}\tilde\chi^{0}_{1}$) (vertical) and BR$(\tilde{g} \to t\bar{t}\tilde\chi^{0}_{1}$) (horizontal) for Gtb models with $m(\tilde\chi^{0}_{1}) = 1$~TeV, obtained from the CC analysis.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Observed exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Expected exclusion limit in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb models obtained from the CC analysis. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm 1 \sigma$ of its theoretical uncertainty.

Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb (right) models obtained from the CC analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.

Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb (right) models obtained from the CC analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.

Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb (right) models obtained from the NN analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.

Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gbb (right) models obtained from the NN analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.

Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt (left) models obtained from the CC analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.

Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt (left) models obtained from the CC analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.

Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt (left) models obtained from the NN analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.

Upper limit at 95\% CL on the cross-section times branching ratio (fb) in the $\tilde{g}$--$\tilde\chi^0_1$ mass plane for the Gtt (left) models obtained from the NN analysis. The numbers give the observed 95\% CL upper limit on the cross section in fb, with the label colour matching the associated best-expected region. Only a lower limit on the excluded cross section (>0.7 fb) is given at some points due to the very small number events expected and observed in the chosen SR. The dashed and solid bold lines show the 95\% CL expected and observed limits, respectively. The shaded bands around the expected limits show the impact of the experimental and background theoretical uncertainties. The dotted lines show the impact on the observed limit of the variation of the nominal signal cross-section by $\pm1\sigma$ of its theoretical uncertainty.

Acceptance for SR-Gtt-0L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-0L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-0L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-0L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-0L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-0L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-0L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-0L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-0L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-0L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-0L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-0L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-0L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-0L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-0L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-0L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1L-B and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1L-M1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1L-M2 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1L-C and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-B and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-B and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-B and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-B and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-M and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-M and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-M and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-M and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-C and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-C and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-C and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-C and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-2100-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-2100-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-2100-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-2100-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1800-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1800-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1800-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1800-1 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-2300-1200 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-2300-1200 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-2300-1200 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-2300-1200 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1900-1400 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gtt-1900-1400 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1900-1400 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gtt-1900-1400 and the $\tilde{g}\rightarrow t\bar{t}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-2800-1400 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-2800-1400 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-2800-1400 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-2800-1400 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-2300-1000 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-2300-1000 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-2300-1000 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-2300-1000 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-2100-1600 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-2100-1600 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-2100-1600 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-2100-1600 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-2000-1800 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Acceptance for SR-Gbb-2000-1800 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-2000-1800 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Efficiency for SR-Gbb-2000-1800 and the $\tilde{g}\rightarrow b\bar{b}\tilde\chi^0_1$ signal process.

Cutflow for the SR-Gtt-0L-B for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-0L-B for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-0L-M1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-0L-M1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-0L-M2 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-0L-M2 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-0L-C for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-0L-C for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1L-B for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1L-B for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1L-M1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1L-M1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1L-M2 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1L-M2 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1L-C for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1L-C for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-B for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-B for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-M for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-M for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-C for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-C for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtb-B for a representative Gtb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtb-B for a representative Gtb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtb-M for a representative Gtb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtb-M for a representative Gtb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtb-C for a representative Gtb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtb-C for a representative Gtb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-2100-1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-2100-1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1800-1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1800-1 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-2300-1200 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-2300-1200 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1900-1400 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gtt-1900-1400 for a representative Gtt signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-2800-1400 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-2800-1400 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-2300-1000 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-2300-1000 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-2100-1600 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-2100-1600 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-2000-1800 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Cutflow for the SR-Gbb-2000-1800 for a representative Gbb signal. Signal was generated with 30000 events. Expected yields are normalised to a luminosity of 139~fb$^{-1}$.

Distribution of $m(e^{\pm},\mu^{\pm})_{\mathrm{lead}}$ in the electron-muon signal region after the background-only fit.

Distribution of $m(\mu^{\pm},\mu^{\pm})_{\mathrm{lead}}$ in the muon-muon signal region after the background-only fit.

Distribution of $m(\ell^{\pm},\ell^{\prime \pm})_{\mathrm{lead}}$ in the three-lepton signal region after the background-only fit.

Event yield in the four-lepton signal region after the background-only fit.

Two-lepton signal region weighted cutflows for electron-electron channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark and multiboson process backgrounds are merged under the 'other' category.

Two-lepton signal region weighted cutflows for electron-muon channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark and multiboson process backgrounds are merged under the 'other' category.

Two-lepton signal region weighted cutflows for muon-muon channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark and multiboson process backgrounds are merged under the 'other' category.

Three-lepton signal region weighted cutflow for $\ell \ell \ell$ channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark and multiboson process backgrounds are merged under the 'other' category.

Four-lepton signal region weighted cutflow for $\ell \ell \ell \ell$ channel. Data and a few representative signal samples are presented alongside the main background contributions. Yield stands for the observed yield in data and the expected yields based on $\sigma \cdot \mathcal{L}$ for the signal and background samples. Top quark, Drell-Yan, and multiboson process backgrounds are merged under the 'other' category.

Probability of finding at least one TAR jet, where the p_T-leading TAR jet passes the m_Wcand and D₂^β=1 requirements, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=2100 GeV, with the preselections applied.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=500 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=1000 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=1700 GeV, with the preselections applied that do not pass the requirements of the merged category.

Probability of finding a W_had candidate reconstructed as a pair of R=0.4 PFlow jets, as a function of m_s. The probability is determined in a sample of signal events with m_Z'=2100 GeV, with the preselections applied that do not pass the requirements of the merged category.

Observed exclusion contour at 95% C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_χ=1, m_χ=200 GeV, and sinθ=0.01.

Expected exclusion contour at 95% C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_χ=1, m_χ=200 GeV, and sinθ=0.01.

Expected+1σ exclusion contour at 95% C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_χ=1, m_χ=200 GeV, and sinθ=0.01.

Expected-1σ exclusion contour at 95% C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_χ=1, m_χ=200 GeV, and sinθ=0.01.

Expected+2σ exclusion contour at 95% C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_χ=1, m_χ=200 GeV, and sinθ=0.01.

Expected-2σ exclusion contour at 95% C.L. for the dark Higgs model in the (m_Z', m_s) plane for g_q=0.25, g_χ=1, m_χ=200 GeV, and sinθ=0.01.

Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W^± W^∓) for m_Z'=0.5 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W^± W^∓) process for m_Z'=0.5 TeV, shown in dashed blue.

Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W^± W^∓) for m_Z'=1 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W^± W^∓) process for m_Z'=1 TeV, shown in dashed blue.

Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W^± W^∓) for m_Z'=1.7 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W^± W^∓) process for m_Z'=1.7 TeV, shown in dashed blue.

Observed upper limits at 95% C.L. on σ(pp → s χχ) × B(s → W^± W^∓) for m_Z'=2.1 TeV as a function of m_s. The expected limits, varied up and down by one and two standard deviations, are shown as green and yellow bands, respectively. The observed and expected limits are compared to the theoretical LO cross section for the σ(pp → s χχ) × B(s → W^± W^∓) process for m_Z'=2.1 TeV, shown in dashed blue.

Data overlaid on SM background yields stacked in each SR and CR category after the fit to data ('Post-fit'). The yields in the SR are broken down into their contributions to the individual bins. The maximum-likelihood estimators are set to the conditional values of the CR-only fit, and propagated to SR and CRs.

Dominant sources of uncertainty for three dark Higgs scenarios after the fit to data. The uncertainties are quantified in terms of their contribution to the fitted signal uncertainty that is expressed relative to the theory prediction. Three representative dark Higgs signal scenarios with g_q=0.25, g_χ=1.0, sinθ=0.01 and m_χ=200 GeV are considered, which are indicated using the (m_Z', m_s) format in units of GeV in the table columns.

Cumulative efficiencies in the merged category for three representative dark Higgs signal scenarios with g_q=0.25, g_χ=1.0, sinθ=0.01, m_Z' = 1 TeV, and m_χ=200 GeV considering s→W(ℓν)W(qq) decays only.

Cumulative efficiencies in the resolved category for three representative dark Higgs signal scenarios with g_q=0.25, g_χ=1.0, sinθ=0.01, m_Z' = 1 TeV, and m_χ=200 GeV considering s→W(ℓν)W(qq) decays only.

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={300, 350, 400, 500, 750} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={1000, 1700} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Theoretical cross section for &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) for each of the dark Higgs signal points at m_Z′ ={2100, 2500, 2900, 3300} GeV, with g_q = 0.25, g<sub>&chi; = 1.0, sin&theta; = 0.01, m_Z′ = 1 TeV , and m_&chi; = 200 GeV. Also shown are experimentally excluded cross sections of &sigma;(pp &rarr; s&chi;&chi;) &times; B(s &rarr; W^&plusmn;W^&#8723;) (Obs.) together with the expectations (Exp.) varied up and down by one standard deviation (&plusmn;1&sigma;).

Summary of the total uncertainty in the background prediction for each SR of the tt0L-low, tt0L-high, tt1L and tt2L analysis channels in the statistical combination. Their dominant contributions are indicated by individual lines. Individual uncertainties can be correlated, and do not necessarily add up in quadrature to the total background uncertainty.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

$E_{\text{T}}^{\text{miss}}$ distribution in SR0X for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.

$E_{\text{T}}^{\text{miss}}$ distribution in SRWX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.

$E_{\text{T}}^{\text{miss}}$ distribution in SRTX for the tt0L-low analysis. The contributions from all SM backgrounds are shown after the profile likelihood simultaneous fit to all tt0L-low CRs, with the hatched bands representing the total uncertainty. The category '$t\bar{t}$ (other)' represents $t\bar{t}$ events without extra jets or events with extra light-flavour jets. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The expected distributions for selected signal models are shown as dashed lines. The overflow events are included in the last bin. The bottom panels show the ratio of the observed data to the total SM background prediction, with the hatched area representing the total uncertainty in the background prediction and the red arrows marking data outside the vertical-axis range.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Associated production of DM with both single top quarks ($tW$ and $tj$ channels) and top quark pairs is considered. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for each individual channel and their statistical combination.

Exclusion limits for colour-neutral scalar mediator dark matter models as a function of the mediator mass $m(\phi)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Exclusion limits for colour-neutral pseudoscalar mediator dark matter models as a function of the mediator mass $m(a)$ for a DM mass $m_{\chi} = 1$ GeV. Only associated production of DM with top quark pairs is considered for this interpretation. The limits are calculated at 95% CL and are expressed in terms of the ratio of the excluded cross section to the nominal cross section for a coupling assumption of $g = g_q = g_{\chi} = 1$. The solid (dashed) lines show the observed (expected) exclusion limits for the tt0L-high and tt0L-low analyses and their statistical combination.

Representative fit distribution in the different flavour leptons signal region for the tt2L analysis: each bin of such distribution, starting from the red arrow, corresponds to a single SR included in the fit. 'FNP' includes the contribution from fake/non-prompt lepton background arising from jets (mainly $\pi/K$, heavy-flavour hadron decays and photon conversion) misidentified as leptons, estimated in a purely data-driven way. 'Other' includes contributions from $t\bar{t}W$, $tZ$ and $tWZ$ processes. The total uncertainty in the SM expectation is represented with hatched bands and the expected distributions for selected signal models are shown as dashed lines.

Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.

Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.

Signal acceptance in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of accepted events at generator level in signal Monte Carlo simulation divided by the total number of events in the sample.

Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$t\bar{t}$ model, defined as the number of selected reconstructed events divided by the acceptance.

Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tW$ model, defined as the number of selected reconstructed events divided by the acceptance.

Signal efficiency in SR0X, SRWX and SRTX for simplified DM+$tj$ model, defined as the number of selected reconstructed events divided by the acceptance.

Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 2045000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$t\bar{t}$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 400000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 120000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tW$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 100000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(\phi, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 169000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SR0X. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRWX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Cutflow for the reference point DM+$tj$ $m(a, \chi) = (10, 1)$ GeV in signal region SRTX. The column labelled 'weighted' shows the event yield including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns the 'weighted' numbers in the first and the second row, labelled 'Total' and 'Filtered', which correspond to $\mathcal{L}\cdot\sigma$ and $\mathcal{L}\cdot\sigma\cdot\epsilon$ expected, respectively. The 'Skim' selection requires the $p_{\text{T}}$ of the leading four jets to be above (80, 60, 40, 40) GeV, the missing transverse momentum $E_{\text{T}}^{\text{miss}} > 140$ GeV, the missing momentum significance $\mathcal{S} > 8$, $\Delta\phi_{\min}(\vec{p}_{\text{T,1-4}},\vec{p}_{\text{T}}^{\text{miss}}) > 0.4$ and a lepton veto. The 'Orthogonalisation' selection is defined in the main body. In total 140000 raw MC events were generated prior to the specified cuts, with the column 'Unweighted yield' collecting the numbers after each cut.

Parametrization of the $A_{X}$ factor, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level, as function of $m_{X}$ obtained from the narrow width simulated signal samples produced in gluon fusion.

The correction factor, $C_{X}$, defined as the ratio between the number of reconstructed signal events passing the analysis cuts and the number of signal events at the particle level generated within the fiducial volume, and acceptance correction factor, $A_{X}$, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level. Both are computed for NWA spin-0 models as a function of $m_{X}$.

Effect of event selections on a scalar MC signal sample generated for $m_{X}$ = 15 GeV and on the data. For the MC sample, the efficiencies are shown after applying event weights and a truth level filter that requires two photons with $p^{\gamma\gamma}_{T}>40$ GeV; for the data, the absolute yields are shown. The initial yields for data include a trigger preselection that is the OR of a list of single photon and diphoton triggers. The "2 $loose$ photons" step includes the kinematic acceptance cuts.

Parameterization of the Double Sided Crystal Ball function parameters describing the scalar mass resolution model as a function of $m_{X}$ [GeV].

Expected 95% CL upper limits with 1-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 300 GeV.

Expected 95% CL upper limits with 2-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 300 GeV.

Observed 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 300 GeV.

Expected 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 600 GeV.

Expected 95% CL upper limits with 1-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 600 GeV.

Expected 95% CL upper limits with 2-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 600 GeV.

Observed 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 600 GeV.

Expected 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 900 GeV.

Expected 95% CL upper limits with 1-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 900 GeV.

Expected 95% CL upper limits with 2-sigma uncertainty on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 900 GeV.

Observed 95% CL upper limits on the production of a heavy Higgs boson as functions of fw and fww with mass equal to 900 GeV.

Observed (black solid curve) and expected (black dashed curve) 95% CL upper limits on the production of a heavy Higgs boson as functions of its mass with ( fW, fWW) fixed at (0, 6200). The green (inner) and yellow (outer) bands represent 1 and 2 sigma uncertainty in the expected limits. Theoretical predictions (red solid curve) as functions of the heavy Higgs boson mass are overlaid.

Observed (black solid curve) and expected (black dashed curve) 95% CL upper limits on the production of a heavy Higgs boson as functions of its mass with ( fW, fWW) fixed at (1350, 0). The green (inner) and yellow (outer) bands represent 1 and 2 sigma uncertainty in the expected limits. Theoretical predictions (red solid curve) as functions of the heavy Higgs boson mass are overlaid.

Pre-fit comparison between data and background in the baseline SR for two of the variables used as input for the BSM pBDT: HT.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR HF e.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR HF mu.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR Conv.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR ttW.

Data and post-fit background comparison for the distributions of the discriminant variables fitted in the control regions obtained with the background-only fit: CR lowBDT.

Data and post-fit background comparison obtained with the background-only fit in the validation region defined for ttW+jets events: number of jets.

Data and post-fit background comparison obtained with the background-only fit in the validation region defined for ttW+jets events: SM BDT.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 400 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 1000 GeV.

Observed (black solid line) and expected (black dashed line) 95% CL upper limits on the ttH/A cross-section times branching fraction of H/A->tt as a function of mH/A.

Observed (red line) and expected (black dashed line) exclusion regions at 95% CL in the tan(beta) versus mass plane assuming that both, a heavy scalar H and pseudo-scalar A, contribute to the tttt final state and have the same mass mH=mA.

Observed (red line) and expected (black dashed line) exclusion regions at 95% CL in the tan(beta) versus mass plane assuming that only the scalar H contributes to the tttt final state.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 500 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 600 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 700 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 800 GeV.

Data and post-fit background comparison obtained with the background-only fit to the BSM SR for the BSM pBDT distribution used for mH = 900 GeV.

Observed (red line) and expected (black dashed line) exclusion regions at 95% CL in the tan(beta) versus mass plane assuming that only the pseudo-scalar A contributes to the tttt final state.

The product of acceptance and efficiency (A×ε), defined as the number of signal events satisfying the full set of selection criteria of the BSM SR, divided by the total number of generated signal events, for the tt̄H(→ tt̄ ) process in a type-II 2HDM for tanβ = 1 for the different heavy Higgs mass hypotheses.

Acceptance times efficiency as a function of resonant mass for each event selection step in the search for a neutral heavy scalar resonance produced in association with a W boson decaying to a charged lepton and a neutrino.

Acceptance times efficiency as a function of resonant mass for each event selection step in the search for a neutral heavy scalar resonance produced in association with a Z boson decaying to charged leptons.

Acceptance times efficiency as a function of resonant mass for each event selection step in the search for a neutral heavy scalar resonance produced in association with a Z boson decaying to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a W boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a W boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a Z boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a Z boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Observed 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Observed 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Observed 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Data and post-fit signal and background from S+B fit for 315 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 315 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 550 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for 550 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for a 790 GeV narrow-width pseudoscalar resonance decaying to a Z boson and a 300 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 790 GeV narrow-width pseudoscalar resonance decaying to a Z boson and a 300 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 420 GeV large-width pseudoscalar resonance decaying to a Z boson and a 320 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 420 GeV large-width pseudoscalar resonance decaying to a Z boson and a 320 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 700 GeV large-width pseudoscalar resonance decaying to a Z boson and a 380 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 700 GeV large-width pseudoscalar resonance decaying to a Z boson and a 380 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for SM VHH production, with each Higgs boson decaying to $2b$.

Data and post-fit signal and background from S+B fit for SM VHH production, with each Higgs boson decaying to $2b$.

Observed 95% CL limits on the production cross-section for scalar up-type LQs decaying into a top quark and a neutrino or a bottom quark and an electron.

Expected 95% CL limits on the production cross-section for scalar up-type LQs decaying into a top quark and a neutrino or a bottom quark and an electron.

Observed 95% CL limits on the production cross-section for scalar down-type LQs decaying into a bottom quark and a neutrino or a top quark and a muon.

Expected 95% CL limits on the production cross-section for scalar down-type LQs decaying into a bottom quark and a neutrino or a top quark and a muon.

Observed 95% CL limits on the production cross-section for scalar down-type LQs decaying into a bottom quark and a neutrino or a top quark and an electron.

Expected 95% CL limits on the production cross-section for scalar down-type LQs decaying into a bottom quark and a neutrino or a top quark and an electron.

Observed 95% CL limits on the production cross-section for vector up-type LQs in the Yang-Mills coupling scenario decaying into a top quark and a neutrino or a bottom quark and a muon.

Expected 95% CL limits on the production cross-section for vector up-type LQs in the Yang-Mills coupling scenario decaying into a top quark and a neutrino or a bottom quark and a muon.

Observed 95% CL limits on the production cross-section for vector up-type LQs in the Yang-Mills coupling scenario decaying into a top quark and a neutrino or a bottom quark and an electron.

Expected 95% CL limits on the production cross-section for vector up-type LQs in the Yang-Mills coupling scenario decaying into a top quark and a neutrino or a bottom quark and an electron.

Observed 95% CL limits on the production cross-section for vector up-type LQs in the minimal coupling scenario decaying into a top quark and a neutrino or a bottom quark and a muon.

Expected 95% CL limits on the production cross-section for vector up-type LQs in the minimal coupling scenario decaying into a top quark and a neutrino or a bottom quark and a muon.

Observed 95% CL limits on the production cross-section for vector up-type LQs in the minimal coupling scenario decaying into a top quark and a neutrino or a bottom quark and an electron.

Expected 95% CL limits on the production cross-section for vector up-type LQs in the minimal coupling scenario decaying into a top quark and a neutrino or a bottom quark and an electron.

Product of signal acceptance and efficiency in the training region for scalar up-type LQs decaying into top quarks and neutrinos or bottom quarks and muons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(LQ_{mix}^u\rightarrow b\mu)$.

Product of signal acceptance and efficiency in the training region for scalar up-type LQs decaying into top quarks and neutrinos or bottom quarks and electrons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(LQ_{mix}^u\rightarrow be)$.

Product of signal acceptance and efficiency in the training region for scalar down-type LQs decaying into top quarks and muons or bottom quarks and neutrinos depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(LQ_{mix}^d\rightarrow t\mu)$.

Product of signal acceptance and efficiency in the training region for scalar down-type LQs decaying into top quarks and electrons or bottom quarks and neutrinos depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(LQ_{mix}^d\rightarrow te)$.

Product of signal acceptance and efficiency in the training region for vector LQs in the Yang-Mills coupling scenario decaying into top quarks and neutrinos or bottom quarks and muons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(vLQ_{mix}^{YM}\rightarrow b\mu)$.

Product of signal acceptance and efficiency in the training region for vector LQs in the Yang-Mills coupling scenario decaying into top quarks and neutrinos or bottom quarks and electrons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(vLQ_{mix}^{YM}\rightarrow be)$.

Product of signal acceptance and efficiency in the training region for vector LQs in the minimal coupling scenario decaying into top quarks and neutrinos or bottom quarks and muons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(vLQ_{mix}^{min}\rightarrow b\mu)$.

Product of signal acceptance and efficiency in the training region for vector LQs in the minimal coupling scenario decaying into top quarks and neutrinos or bottom quarks and electrons depending on the signal mass, $m_{LQ}$, and $\mathcal{B}(vLQ_{mix}^{min}\rightarrow be)$.

Cut-flow for the four models containing scalar LQs at a mass of $m_{LQ}=1300$ GeV and $\mathcal{B}(LQ_{mix}\rightarrow q_3\ell)=0.5$.

Cut-flow for the four models containing vector LQs at a mass of $m_{LQ}=1300$ GeV and $\mathcal{B}(LQ_{mix}\rightarrow q_3\ell)=0.5$.

Results of applying the BH algorithm to the mass spectra in the leading photon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading electron category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading muon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading small-R jet category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading bjet category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading large-R jet category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading photon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading electron category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the leading muon category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the inclusive category, using the fitted background estimations from the initial step

Results of applying the BH algorithm to the mass spectra in the inclusive category, using the fitted background estimations from the initial step

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading small-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading bjet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading large-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading photon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading electron category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading muon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading small-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading bjet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading large-R jet category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading photon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading electron category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the leading muon category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the inclusive category

Upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signal with a relative width value of 3% as a function of mass in the inclusive category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading small-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading bjet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading large-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading photon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading electron category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading muon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading small-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading bjet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading large-R jet category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading photon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading electron category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the leading muon category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the inclusive category

Comparison of observed upper limits at 95% CL on the cross section times branching fraction times acceptance for a Gaussian-shaped signals with relative width values as a function of mass in the inclusive category

Acceptance times efficiency in an HVT model as a function of mass in the leading large-R jet category

Upper limits at 95% CL on the cross section times the branching fraction($W^{\prime} \to ZW$) for the HVT signal as functions of mass($m_{ZX}$) in the leading large-R jet category.The full(dashed) red curves correspond to the theoretical predictions of HVT model A(B)

Comparison of the identification efficiencies using standard and merged-ee reconstruction as a function of true PT(Z)

Background rejection factor as a function of signal efficiency. The red curve shows the BDT performance whereas the blue curve corresponds to that of a cut-based analysis relying only on the jet energyfraction deposited in the EM calorimeter

BH p-values of the 100 pseudo-experiments as a function of in the leading small-R jet category for an injected Gaussian-shaped signal with a relative width value of 3%. The fractions of the pseudo-experiments that have the correctly identified interval and BH p-values below the threshold of 0.01 are indicated. The background is derived by the background-only fit in the full fitting range.

BH p-values of the 100 pseudo-experiments as a function of in the leading small-R jet category for an injected Gaussian-shaped signal with a relative width value of 3%. The fractions of the pseudo-experiments that have the correctly identified interval and BH p-values below the threshold of 0.01 are indicated. The background is derived by the background-only fit in the range excluding the BH interval.

Fractions of pseudo-experiments in which the detected BH interval agrees with the injected mass point and the BH p-value is below 0.01 as a function of mass in the leading small-R jet category for Gaussian-shaped signal with a relative width of 3%.

Fractions of pseudo-experiments in which the detected BH interval agrees with the injected mass point and the BH p-value is below 0.01 as a function of mass in the leading small-R jet category for Gaussian-shaped signal with a relative width of 3%.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 3% at the low boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 3% at the high boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 5% at the low boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 5% at the high boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 10% at the low boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Distribution of exclusion upper limits on signal event yields at 95% CL from 1000 pseudo-experiments for Gaussian-shaped signals with relative width values of 10% at the high boundary of the limit-sensitive mass range for the ZX spectrum of the leading small-R jet category. The vertical line corresponds to the expected nominal exclusion limit at 95% CL.

Data yields of the six event categories in the $Z\to e^+e^-$ and $\mu^+\mu^-$ decay channels. The merged-$e^+e^-$ events are included in the $e^+e^-$ channel, increasing the event yield, mainly in the leading large-$R$-jet category, by 0.6 %.

A list of mass spectra, event categories and their corresponding fit ranges, functional forms, numbers of free parameters and global $\chi^2$ $p$-values from background-only fits. The fit range values are rounded to the nearest 5 GeV. Here $f_1(x)=p_0\left(\mathrm{e}^{-p_1x}+p_2\mathrm{e}^{-(p_1+p_3)x}+p_4\mathrm{e}^{-(p_1+p_3+p_5)x}+\cdots\right)$ and $f_2(x)=p_0(1-x)^{p_1}x^{p_2+p_3\ln\!x+p_4\ln^2\!x+\cdots}$

A list of mass spectra, event categories and their corresponding signal-sensitive mass ranges. The initial BH $p$-value is obtained by using the background derived from the background-only fit in the full fit range, whereas the new BH $p$-value uses the background derived from the background-only fit in the range excluding the initial BH interval.

A list of mass spectra, event categories, relative width values of Gaussian-shaped signals and limit-sensitive mass ranges and fractions, corresponding to the mass values in the previous column, of pseudo-experiments having exclusion upper limits higher than the nominal exclusion limit at 95% CL

Cutflow of HVT model $A$ signals ($W^\prime \to ZW \to \ell\ell qq$) with $m_{W^\prime} = 1$ TeV and $m_{W^\prime} = 4$ TeV based on MC simulations.

Acceptance times efficiency ($\mathcal{A} \times \epsilon$) values in % in the dominant event category for $p^Z_{\mathrm{T}} > 100$ GeV in the $Z \to \ell^{+}\ell^{-}$ decay channel.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-0J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.

Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-1J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.

Observed and expected exclusion limits on SUSY simplified models, with observed upper limits on signal cross-section (fb) overlaid, for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

The upper panel shows the observed number of events in each of the binned SRs defined in Table 3, together with the expected SM backgrounds obtained after applying the efficiency correction method to compute the number of expected FSB events. `Others' include the non-dominant background sources, e.g. $t \bar{t}$+$V$, Higgs boson and Drell--Yan events. The uncertainty band includes systematic and statistical errors from all sources. The distributions of two signal points with mass splittings $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 30$ GeV and $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 50$ GeV are overlaid. The lower panel shows the significance as defined in Ref. [115].

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.785,0.79]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.785,0.79]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,0.795]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,0.795]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.795,0.80]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.795,0.80]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,0.81]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,0.81]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.81,1]$ inclusive region. Acceptance is calculated by applying the signal region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

Cutflow table for the chargino signal sample with $m\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0=(125,25)$ GeV, in the SR-SF BDT-signal$\in (0.77,1]$ and SR-DF BDT-signal$\in (0.81,1]$ regions. The yields include the process cross-section and are weighted to the 139 fb$^{-1}$ luminosity. 170000 events were generated for the sample.

Observed and expected exclusion limits on SUSY simplified models, with observed upper limits on signal cross-section (fb) overlaid, for chargino-pair production with $W$-boson-mediated decays in the $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ plane. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

The upper panel shows the observed number of events in the SRs defined in Table 3, together with the expected SM backgrounds obtained after the background fit in the CRs. `Others' include the non-dominant background sources, e.g.$t \bar{t}$+$V$, Higgs boson and Drell--Yan events. The uncertainty band includes systematic and statistical errors from all sources. Distributions for three benchmark signal points are overlaid for comparison. The lower panel shows the significance as defined in Ref. [115].

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the hadhad category. The efficiencies are calculated with respect to HH to bbtautau decays in which both tau-leptons decay hadronically. The ‘Pre-selection’ consists of basic requirements, including that at least two tau-had-vis pass loose kinematic requirements, at least one of the tau-had-vis candidate passes identification requirement, and that events do not contain an electron or muon. The ‘Object selections’ require exactly two tau-had-vis candidates, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Loose tau ID’ requires that both tau-had-vis candidates pass identification requirements. The ‘DTT offline jet cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad SLT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad LTT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Post-fit expected number of signal and background events and observed number of data events after applying the selection criteria and requiring exactly 2 b-tagged jets and assuming a background-only hypothesis.

Observed and expected limits at 95% CL on the cross-section of HH production, for the non-resonant ggF+VBF HH search, and the resonant HH search for four values of the resonance mass mX.

Acceptance times efficiency for the full analysis selections as a function of the resonance mass mX in the hadhad, lephad SLT and lephad LTT trigger categories, and the combined lephad. The acceptance times efficiency is evaluated for HH to bbtautau decays, with respect to the targeted tau-lepton decay modes (lephad or hadhad).

Post-fit distribution of mHH in the hadhad channel.

Post-fit distribution of mHH in the lephad SLT channel.

Post-fit distribution of mHH in the lephad LTT channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of of di-b-jet mass in the hadhad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

BDT for SM HH in the hadhad channel

NN for SM HH in the lephad SLT channel

NN for SM HH in the lephad LTT channel

PNN for mX = 500 GeV resonant HH in the hadhad channel

PNN for mX = 500 GeV resonant HH in the lephad SLT channel

PNN for mX = 500 GeV resonant HH in the lephad LTT channel

PNN for mX = 1000 GeV resonant HH in the hadhad channel

PNN for mX = 1000 GeV resonant HH in the lephad SLT channel

PNN for mX = 1000 GeV resonant HH in the lephad LTT channel

Event yields as a function of log10(S/B) for data, background and non-resonant HH signal. Final discriminant bins from the hadhad, lephad SLT and lephad LTT categories are combined into bins of log10(S/B). The B is the fitted background yield assuming background-only hypothesis, and the signal S is scaled to the SM expected cross-section.

Observed and expected limits at 95% CL on the cross-section of the resonant HH production as a function of the scalar resonance mass mX.

Post-fit distribution of delta-R between the taus in the hadhad channel.

Post-fit distribution of delta-R between the b-tagged jets in the hadhad channel.

Post-fit distribution of delta-R between the taus in the lephad SLT channel.

Post-fit distribution of delta-R between the b-tagged jets in the lephad SLT channel.

Post-fit distribution of delta-pT between the tau and lepton in the lephad SLT channel.

Post-fit distribution of pT of the subleading b-tagged jet in the lephad SLT channel.

Post-fit distribution of MTW in the lephad SLT channel.

Post-fit distribution of missing transverse momentum in the lephad SLT channel.

Post-fit distribution of missing transverse momentum centrality in the lephad SLT channel.

Post-fit distribution of delta-phi between the Higgs boson candidates in the lephad SLT channel.

Post-fit distribution of delta-pT between the tau and lepton in the lephad LTT channel.

Post-fit distribution of delta-R between the taus in the lephad LTT channel.

Post-fit distribution of delta-phi between the lepton and the missing transverse momentum in the lephad LTT channel.

Post-fit distribution of delta-phi between the Higgs boson candidates in the lephad LTT channel.

Post-fit distribution of the total transverse momentum s in the lephad LTT channel.

PNN for mX = 300 GeV resonant HH in the hadhad channel

PNN for mX = 300 GeV resonant HH in the lephad SLT channel

PNN for mX = 300 GeV resonant HH in the lephad LTT channel

PNN for mX = 1600 GeV resonant HH in the hadhad channel

PNN for mX = 1600 GeV resonant HH in the lephad SLT channel

PNN for mX = 1600 GeV resonant HH in the lephad LTT channel

Local p-value of the background-only hypothesis as a function of the resonance mass.

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the signal region, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $W$+jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Expected distributions of the MEM discriminant $P(S|X)$ in the SR, for the s-channel single-top signal, and for the $t\bar{t}$ and $W$+jets backgrounds, for MEM discriminant values larger than $2.0\times10^{-4}$. Each distribution is normalised to unity. The binning is the same as the optimised binning used in the signal extraction fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $W$+jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 3-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 4-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty bands indicate the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR before the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore not included. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$, after subtraction of all backgrounds. The fitted distribution for the simulation of the signal is shown together with the post-fit uncertainty in the backgrounds. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Pre-fit and post-fit event yields in the SR, for MEM discriminant values larger than $2.0\times10^{-4}$. The central value of the event yield for each process is calculated by summing the values of the discriminant bin contents, using the nominal expected yield for the pre-fit value, and the best-fit estimate for the post-fit value. The error includes statistical and systematic uncertainties summed in quadrature. All sources of systematic uncertainties are included, taking into account correlations and anti-correlations in the post-fit case. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore only included in the post-fit uncertainties.

Observed impact of the different sources of uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. The 'Systematic uncertainties' category combines all sources of systematic uncertainties. The statistical uncertainty is obtained by repeating the fit after having fixed all nuisance parameters, including the $t\bar{t}$ and $W$+jets normalisation factors. 'Total' gives the total uncertainty on the measurement.

Observed impact of the different sources of $t\bar{t}$ modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and 'ME/PS matching' to the matching of the ME to the parton shower.

Observed impact of the different sources of s-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of t-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of $tW$ modelling uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and '$t\bar{t}$ overlap' to the algorithm removing the overlap between $tW$ and $t\bar{t}$ production at NLO, as described in Section 7 in the paper.

Observed impact of the different sources of PDF uncertainties on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the ${E}_{T}^{miss}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the $m_{T}^{W}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Nuisance parameters ranked according to their post-fit impacts on the best-fit value of the ratio $\mu$ of the measured cross-section to the predicted cross-section. In the figure, only the 20 nuisance parameters with the largest post-fit impacts are shown. The empty (solid) blue rectangles illustrate the pre-fit (post-fit) impact on $\mu$, corresponding to the upper axis. The pre-fit (post-fit) impact of each nuisance parameter, $\Delta\mu$, is calculated as the difference in the fitted value of $\mu$ between the nominal fit and the fit when fixing the corresponding nuisance parameter to $\hat{\theta}\pm\Delta\theta$ ($\hat{\theta}\pm\Delta\hat{\theta}$), where $\hat{\theta}$ is the best-fit value of the nuisance parameter and $\Delta\theta$ ($\Delta\hat{\theta}$) is its pre-fit (post-fit) uncertainty. Several systematic uncertainties are split into different nuisance parameters, which are indicated by NP. JES (JER) indicates jet energy scale (resolution), and $\gamma$ indicates a nuisance parameter associated to the MC statistics in one of the 18 bins numbered from 0 to 17. The black points show the best-fit values of the nuisance parameters, with the error bars representing the post-fit uncertainties. Each nuisance parameter is shown wrt. its nominal value, $\theta_0$, and in units of its pre-fit uncertainty, except the free-floating normalisation factors of the $t\bar{t}$ and $W$+jets backgrounds, and the parameters associated to the MC statistics in each bin, for which the post-fit values and uncertainties are shown.

Correlation matrix of the nuisance parameters and of the ratio $\mu$ of the measured cross-section to the predicted cross-section. The correlations are given after the fit to data. In the figure, only the parameters which have a correlation of at least 0.2 with any other parameter are shown.

Distribution of the MEM discriminant $P(S|X)$ in the SR for MEM discriminant values larger than $2.0\times10^{-4}$, for the collision data used for the measurement, and for 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.

Measured values of the signal cross-section and of the $t\bar{t}$ and $W$+jets normalisation factors, obtained by statistical-only fits to the collision data used for the measurement, and to 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The central values and their statistical uncertainties are obtained by repeating the fit after having fixed all nuisance parameters, except the $t\bar{t}$ and $W$+jets normalisation factors, which are let free-floating (unlike for the statistical uncertainty on the cross-section quoted in the paper). The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.

Naming convention for the observables at different levels of the analysis. At the background-subtracted level the contributions of tracks from pile-up collisions and tracks from secondary vertices are subtracted. At the corrected level the tracking-efficiency correction (TEC) is applied. The observables at particle level are the analysis results.

The total pile-up scale-factor relative uncertainty parameterised in $\mu$ and $n_\text{trk,out}$ and expressed in percent.

The $\chi^2$ and NDF for measured normalised differential cross-sections obtained by comparing the different predictions with the unfolded data. Global($n_\text{ch},\Sigma_{n_{\text{ch}}} p_{\text{T}}$) denotes the scenario in which the covariance matrix is built including the correlations of systematic uncertainties between the two observables $n_{\text{ch}}$ and $\Sigma_{n_{\text{ch}}} p_{\text{T}}$

Normalised differential cross-section as a function of $n_\text{ch}$.

Normalised differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$.

Normalised double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n\text{ch}$ in $ n_\text{ch} \geq 80$.

The $\chi^2$ and NDF for the measured normalised differential cross-sections obtained by comparing the different predictions with the unfolded data. The values corresponding to the `Total' scenario are given. In this scenario, only uncertainties described in Section 8 of the paper are included, while the extra theory uncertainties defined in Section 9 are omitted.

The $\chi^2$ and NDF for the measured normalised differential cross-sections obtained by comparing the different predictions with the unfolded data. The values corresponding to the `De-correlate modelling' scenario are given. This scenario uses the detector covariance matrix, and adding modelling uncertainties, scale variations in the matrix element and parton shower as well as the $h_{\text{damp}}$ variation only to the diagonal elements of the covariance matrix.

The $\chi^2$ and NDF for measured absolute differential cross-sections obtained by comparing the different predictions with the unfolded data. Global($n_\text{ch},\Sigma_{n_{\text{ch}}} p_{\text{T}}$) denotes the scenario in which the covariance matrix is built including the correlations of systematic uncertainties between the two observables $n_{\text{ch}}$ and $\Sigma_{n_{\text{ch}}} p_{\text{T}}$

The $\chi^2$ and NDF for the measured absolute differential cross-sections obtained by comparing the different predictions with the unfolded data. The values corresponding to the `Total' scenario are given. In this scenario, only uncertainties described in Section 8 of the paper are included, while the extra theory uncertainties defined in Section 9 are omitted.

The $\chi^2$ and NDF for the measured normalised differential cross-sections obtained by comparing the different predictions with the unfolded data. The values corresponding to the `De-correlate modelling' scenario are given. This scenario uses the detector covariance matrix, and adding modelling uncertainties, scale variations in the matrix element and parton shower as well as the $h_{\text{damp}}$ variation only to the diagonal elements of the covariance matrix.

Absolute differential cross-section as a function of $n_\text{ch}$.

Absolute differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$.

Absolute double-differential cross-section as a function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n\text{ch}$ in $ n_\text{ch} \geq 80$.

Covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix of the normalised differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ vs. $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix of the normalised differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the normalised double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ vs. $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the normalised differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix of the absolute differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ vs. $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical, systematic uncertainties, and uncertainties in the theoretical predictions.

Covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix of the absolute differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 40 \leq n_\text{ch} < 60$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 60 \leq n_\text{ch} < 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} < 20$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $ 20 \leq n_\text{ch} < 40$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $40 \leq n_\text{ch} < 60$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $60 \leq n_\text{ch} < 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Covariance matrix between the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ and the absolute double-differential cross-section as function of $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ in $n_\text{ch} \geq 80$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $n_\text{ch}$ vs. $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ vs. $n_\text{ch}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Global covariance matrix of the absolute differential cross-section as function of $n_\text{ch}$ and $\sum_{n_{\text{ch}}} p_{\text{T}}$ in $\sum_{n_{\text{ch}}} p_{\text{T}}$ at particle level, accounting for the statistical and systematic uncertainties, but excluding uncertainties in the theoretical predictions.

Correlation matrix for the individual sources of systematic uncertainty considered in the analysis, for the combined opposite sign (OS) and same sign (SS) binned-template profile likelihood fit to data. The OS or SS refers to the charge signs of the primary lepton and the soft muon. The gamma parameters are NPs used to describe the effect of the limited statistics of the samples.

The $m_{jj}$ distributions for the CRs and the BDT classifier response distribution for the SR after the fit in all regions. The dashed line shows the total background distribution before the fit. The vertical error bars on the data points correspond to the data's statistical uncertainty. Overflows are included in the last bin. The uncertainty band corresponds to the combination of the MC statistical uncertainty and systematic uncertainties obtained in the fit.

The $m_{jj}$ distributions for the CRs and the BDT classifier response distribution for the SR after the fit in all regions. The dashed line shows the total background distribution before the fit. The vertical error bars on the data points correspond to the data's statistical uncertainty. Overflows are included in the last bin. The uncertainty band corresponds to the combination of the MC statistical uncertainty and systematic uncertainties obtained in the fit.

The $m_{jj}$ distributions for the CRs and the BDT classifier response distribution for the SR after the fit in all regions. The dashed line shows the total background distribution before the fit. The vertical error bars on the data points correspond to the data's statistical uncertainty. Overflows are included in the last bin. The uncertainty band corresponds to the combination of the MC statistical uncertainty and systematic uncertainties obtained in the fit.

Observed and expected event yields for the signal and all of the background processes considered in this analysis after the fit to the data in all of the regions. The uncertainty on the expected yield is the combination of statistical and systematic uncertainties obtained in the fit. The individual uncertainties can be correlated and do not necessarily add in quadrature to equal the total background uncertainty.

Summary of the event yield for processes in all regions, after the fit over all regions. The dashed line shows the total background distribution before the fit. The vertical error bars on the data points correspond to the data's statistical uncertainty. The uncertainty band corresponds to the combination of the MC statistical uncertainty and systematic uncertainties obtained in the fit.

Impact of different components of systematic uncertainty on the measured cross section, without taking into account the correlations. The impact is calculated by fixing the value of the corresponding nuisance parameters to the values obtained in the fit used to measure the cross section, performing the fit, estimating the signal strength uncertainty and subtracting it from the nominal uncertainty in quadrature.

Fitted POI values for this analysis, the previous ATLAS analysis, and their combination. The first and second columns present the values obtained in the individual analyses. The third column presents the values obtained in the combination.

Observed and expected one-dimensional limits on dimension 8 aQGC parameters. Limits are obtained by setting all aQGCs parameters except one to zero. Unitarity is not preserved.

Observed and expected one-dimensional limits on dimension 8 aQGC parameters in the region, where unitarity is preserved. Cutoff scales in MC, $E_{c}$, are given for each parameter. Limits are obtained by setting all aQGCs parameters except one to zero.

The $E_{T}^{\gamma}$ distribution in the SR after the fit in the control regions. The red (green) line shows the expected number of events in the case of non-zero EFT coefficient $f_{T0}/\Lambda^4$ ($f_{M0}/\Lambda^4$) with the value shown in the legend. The vertical error bars on the data points correspond to the data statistical uncertainty. Overflows are included in the last bin. The uncertainty band corresponds to the combination of the MC statistical uncertainty and systematic uncertainties obtained in the fit.

Evolution of the expected (red line) and observed (blue line) limits versus $E_{c}$ values for $f_{T0}/\Lambda^4$. The unitarity bound is shown by the black line. The $E_{c}<4$ TeV regime was obtained with $E_{T}^{\gamma} > 600$ GeV. The $E_{c} \geq 4$ TeV regime was obtained with $E_{T}^{\gamma} > 900$ GeV.

Evolution of the expected (red line) and observed (blue line) limits versus $E_{c}$ values for $f_{T5}/\Lambda^4$. The unitarity bound is shown by the black line. The $E_{c}<4$ TeV regime was obtained with $E_{T}^{\gamma} > 600$ GeV. The $E_{c} \geq 4$ TeV regime was obtained with $E_{T}^{\gamma} > 900$ GeV.

Evolution of the expected (red line) and observed (blue line) limits versus $E_{c}$ values for $f_{T8}/\Lambda^4$. The unitarity bound is shown by the black line. The $E_{c}<4$ TeV regime was obtained with $E_{T}^{\gamma} > 600$ GeV. The $E_{c} \geq 4$ TeV regime was obtained with $E_{T}^{\gamma} > 900$ GeV.

Evolution of the expected (red line) and observed (blue line) limits versus $E_{c}$ values for $f_{T9}/\Lambda^4$. The unitarity bound is shown by the black line. The $E_{c}<4$ TeV regime was obtained with $E_{T}^{\gamma} > 600$ GeV. The $E_{c} \geq 4$ TeV regime was obtained with $E_{T}^{\gamma} > 900$ GeV.

Evolution of the expected (red line) and observed (blue line) limits versus $E_{c}$ values for $f_{M0}/\Lambda^4$. The unitarity bound is shown by the black line. The $E_{c}<4$ TeV regime was obtained with $E_{T}^{\gamma} > 400$ GeV. The $E_{c} \geq 4$ TeV regime was obtained with $E_{T}^{\gamma} > 900$ GeV.

Evolution of the expected (red line) and observed (blue line) limits versus $E_{c}$ values for $f_{M1}/\Lambda^4$. The unitarity bound is shown by the black line. The $E_{c}<4$ TeV regime was obtained with $E_{T}^{\gamma} > 400$ GeV. The $E_{c} \geq 4$ TeV regime was obtained with $E_{T}^{\gamma} > 900$ GeV.

Evolution of the expected (red line) and observed (blue line) limits versus $E_{c}$ values for $f_{M2}/\Lambda^4$. The unitarity bound is shown by the black line. The $E_{c}<4$ TeV regime was obtained with $E_{T}^{\gamma} > 400$ GeV. The $E_{c} \geq 4$ TeV regime was obtained with $E_{T}^{\gamma} > 900$ GeV.

Correlation matrix of the parameters for the fit in all regions. Only parameters with absolute value of the correlation coefficient larger than 10 are shown.

The unfolded differential charge asymmetry as a function of the transverse momentum of the top pair system. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.

The unfolded differential charge asymmetry as a function of the longitudinal boost of the top pair system. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NNLO in QCD and NLO in EW theory are listed. The scale uncertainty is obtained by varying renormalisation and factorisation scales independently by a factor of 2 or 0.5 around $\mu_0$ to calculate the maximum and minimum value of the asymmetry, respectively. The nominal value $\mu_0$ is chosen as $H_T/4$. The variations in which one scale is multiplied by 2 while the other scale is divided by 2 are excluded. Finally, the scale and MC integration uncertainties are added in quadrature.

The unfolded inclusive leptonic asymmetry. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

The unfolded differential leptonic asymmetry as a function of the invariant mass of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

The unfolded differential leptonic asymmetry as a function of the transverse momentum of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

The unfolded differential leptonic asymmetry as a function of the longitudinal boost of the di-lepton pair. The unfolded $A_C^{\ell\bar{\ell}}$ is obtained in the reduced phase-space defined by the requirement $|\Delta |\eta_{\ell\bar{\ell}}||<2.5$. The measured values are given with statistical and systematic uncertainties. The SM theory predictions calculated at NLO in QCD and NLO in EW theory are listed. The theory uncertainty is obtained by varying both scales by a factor of 0.5 or 2.0 to calculate the minimum and maximum value of the asymmetry, respectively.

Individual 68% and 95% CL bounds on the relevant Wilson coefficients of the SM Effective Field Theory in units of $\text{TeV}^{-2}$. The bounds are derived from the $A_C^{t\bar{t}}$ inclusive measurement. The experimental uncertainties are accounted for, in the form of the complete covariance matrix that keeps track of correlations between bins for the differential measurement. The theory uncertainty from the NNLO QCD + NLO EW calculation is included by explicitly varying the renormalization and factorization scales, or the parton density functions, in the calculation and registering the variations in the intervals.

Individual 68% and 95% CL bounds on the relevant Wilson coefficients of the SM Effective Field Theory in units of $\text{TeV}^{-2}$. The bounds are derived from the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement. The experimental uncertainties are accounted for, in the form of the complete covariance matrix that keeps track of correlations between bins for the differential measurement. The theory uncertainty from the NNLO QCD + NLO EW calculation is included by explicitly varying the renormalization and factorization scales, or the parton density functions, in the calculation and registering the variations in the intervals.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ inclusive measurement. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0,0.3]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.3,0.6]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.6,0.8]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $\beta_{z,t\bar{t}}$ measurement for $\beta_{z,t\bar{t}}$ $\in$ [0.8,1]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ < 500 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [500,750] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [750,1000] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ $\in$ [1000,1500] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement for $m_{t\bar{t}}$ > 1500 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ < 30 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ $\in$ [30,120] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement for $p_{T,t\bar{t}}$ > 120 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ inclusive measurement. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0,0.3]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.3,0.6]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.6,0.8]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $\beta_{z,\ell\bar{\ell}}$ measurement for $\beta_{z,\ell\bar{\ell}}$ $\in$[0.8,1]. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ < 200 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ $\in$ [200,300] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ $\in$ [300,400] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $m_{\ell\bar{\ell}}$ measurement for $m_{\ell\bar{\ell}}$ > 400 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ < 20 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ $\in$ [20, 70] GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Ranking of the systematic uncertainties with marginalisation for the $A_C^{\ell\ell}$ vs $p_{T,\ell\bar{\ell}}$ measurement for $p_{T,\ell\bar{\ell}}$ > 70 GeV. The effect on unfolded $A_C$ for down and up variation of the systematic uncertainty is shown, respectively. The pulls and constraints of the ranked NPs are obtained from data.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ inclusive measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $m_{t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.

Post-marginalisation correlation coefficients $\rho_{ij}$ of nuisance parameters for the $A_C^{t\bar{t}}$ vs $p_{T,t\bar{t}}$ measurement. Only $|\rho_{ij}| > 0.05$ values are included.