SR1 diboson invariant mass distribution in the resolved tagged category after fitting the signal and sideband regions using a signal (ALP linear ZZ) plus background model. The last bin includes events with diboson invariant masses up to 3000 GeV. The signal is normalized to the 95% CL cross section limit at $f_a$ = 3 TeV (the scale factor used in the original figure for better visibility is not applied here).

SR2 diboson invariant mass distribution in the boosted untagged category after fitting the signal and sideband regions using a signal (ALP chiral ZH) plus background model. The last bin includes events with diboson invariant masses up to 3000 GeV. The signal is normalized to the 95% CL cross section limit at $f_a$ = 3 TeV (the scale factor used in the original figure for better visibility is not applied here).

SR2 diboson invariant mass distribution in the boosted tagged category after fitting the signal and sideband regions using a signal (ALP chiral ZH) plus background model. The last bin includes events with diboson invariant masses up to 3000 GeV. The signal is normalized to the 95% CL cross section limit at $f_a$ = 3 TeV.

SR2 diboson invariant mass distribution in the resolved untagged category after fitting the signal and sideband regions using a signal (ALP chiral ZH) plus background model. The last bin includes events with diboson invariant masses up to 3000 GeV. The signal is normalized to the 95% CL cross section limit at $f_a$ = 3 TeV (the scale factor used in the original figure for better visibility is not applied here).

SR2 diboson invariant mass distribution in the resolved tagged category after fitting the signal and sideband regions using a signal (ALP chiral ZH) plus background model. The last bin includes events with diboson invariant masses up to 3000 GeV. The signal is normalized to the 95% CL cross section limit at $f_a$ = 3 TeV (the scale factor used in the original figure for better visibility is not applied here).

Observed and expected 95% CL upper limit on $\sigma \mathcal{B}$(G -> ZZ) as a function of the resonance mass, taking into account all statistical and systematic uncertainties. The electron and muon channels and the various categories used in the analysis are combined. The inner green band and the outer yellow band represent the 68 and 95% coverage of the expected limits in the background-only hypothesis.

Observed and expected 95% CL upper limit on $\sigma \mathcal{B}$(W' -> ZW) as a function of the resonance mass, taking into account all statistical and systematic uncertainties. The electron and muon channels and the various categories used in the analysis are combined. The inner green band and the outer yellow band represent the 68 and 95% coverage of the expected limits in the background-only hypothesis.

Observed and expected 95% CL upper limit on the product of the ALP coupling to gluons and the coupling to Z bosons as a function of the mass scale $f_a$ for ALP masses smaller than 100 GeV.

Observed and expected 95% CL upper limit on the product of the ALP coupling to gluons and the chiral $a_\widetilde{2D}$ coupling as a function of the mass scale $f_a$ for ALP masses smaller than 100 GeV.

Upper limits on the production cross section times branching fraction of the b* RH hypothesis at a 95% CL. Dashed colored lines show the expected limits from the l+jets and all-hadronic channels, where the latter start at resonance masses of 1.4 TeV. The observed and expected limits from the combination are shown as solid and dashed black lines, respectively. The green and yellow bands show the 68 and 95% confidence intervals on the combined expected limits.

Upper limits on the production cross section times branching fraction of the b* VL hypothesis at a 95% CL. Dashed colored lines show the expected limits from the l+jets and all-hadronic channels, where the latter start at resonance masses of 1.4 TeV. The observed and expected limits from the combination are shown as solid and dashed black lines, respectively. The green and yellow bands show the 68 and 95% confidence intervals on the combined expected limits.

Upper limits on the production cross section times branching fraction of the B+b hypothesis at a 95% CL. Dashed colored lines show the expected limits from the l+jets and all-hadronic channels, where the latter start at resonance masses of 1.4 TeV. The observed and expected limits from the combination are shown as solid and dashed black lines, respectively. The green and yellow bands show the 68 and 95% confidence intervals on the combined expected limits.

Upper limits on the production cross section times branching fraction of the B+t hypothesis at a 95% CL. Dashed colored lines show the expected limits from the l+jets and all-hadronic channels, where the latter start at resonance masses of 1.4 TeV. The observed and expected limits from the combination are shown as solid and dashed black lines, respectively. The green and yellow bands show the 68 and 95% confidence intervals on the combined expected limits.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $0.5 < |y| < 1.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.0 < |y| < 1.5$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.0 < |y| < 1.5$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.5 < |y| < 2.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.5 < |y| < 2.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $|y| < 0.5$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $|y| < 0.5$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $0.5 < |y| < 1.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $0.5 < |y| < 1.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.0 < |y| < 1.5$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.0 < |y| < 1.5$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.5 < |y| < 2.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.5 < |y| < 2.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The corrections were calculated by Dittmaier, Huss, Speckner.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The values correspond to the average of the corrections obtained with \textsc{{PYTHIA}}~8 and with \textsc{{HERWIG}}++.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin for the $\text{t}\bar{\text{t}}$ CR. Uncertainties include both the statistical and systematic components.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the low-MET bin for the WZ-enriched region. Uncertainties include both the statistical and systematic components.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin for the WZ-enriched region. Uncertainties include both the statistical and systematic components.

The post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin for the SS CR. Uncertainties include both the statistical and systematic components.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the low-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the med-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the high-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Ewk SR: the post-fit distribution of the $M(\ell\ell)$ variable is shown for the ultra-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 3$\ell$-Ewk search regions: the post-fit distribution of the $M^{\text{min}}_{\text{SFOS}}(\ell\ell)$ variable is shown for the low-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 3$\ell$-Ewk search regions: the post-fit distribution of the $M^{\text{min}}_{\text{SFOS}}(\ell\ell)$ variable is shown for the high-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the TChiWZ and the simplified higgsino models in the scenario where the product of $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ eigenvalues is positive and negative, respectively. The numbers after the model name in the legend indicate the mass of the NLSP and the mass splitting between the NLSP and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the low-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the med-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the high-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The 2$\ell$-Stop SR: the post-fit distribution of the leading lepton $p_{T}$ variable is shown for the ultra-MET bin. Uncertainties include both the statistical and systematic components. The signal distributions overlaid on the plot are from the T2bff$\tilde{\chi}^0_1$ and the T2bW models. The numbers after the model name in the legend indicate the mass of the top squark and the mass splitting between the top squark and LSP, in GeV.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the TChiWZ model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. Results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2} \widetilde{m}_{\tilde{\chi}^0_1} > 0$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the TChiWZ model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. Results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2} \widetilde{m}_{\tilde{\chi}^0_1} < 0$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the simplified higgsino model. The simplified model includes both neutralino pair and neutralino-chargino production modes. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the pMSSM higgsino model. The pMSSM one includes all possible production modes. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The results are reported for the $\widetilde{m}_{\tilde{\chi}^0_2}\widetilde{m}_{\tilde{\chi}^0_1}$ $M(\ell\ell)$ spectrum reweighting scenario. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the T2bff$\tilde{\chi}^0_1$ simplified model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

The observed 95% CL exclusion contours (black curves) assuming the NLO+NLL cross sections, with the variations (thin lines) corresponding to the uncertainty in the cross section for the T2bW simplified model. The red curves present the 95% CL expected limits with the band (thin lines) covering 68% of the limits in the absence of signal. The range of luminosities of the analysis regions included in the fit is indicated on the plot.

Exponent according to the Eq. 5 as a function of transverse momenta extracted from p+Au/p+Al and $^{3}$HeAu/p+Au collision systems. The uncertainties from the $N_{coll}$ calculations and from the overall normalization of p+p cancel in these ratios

Nuclear modification factors in p+Al, p+Au, d+Au, and $^{3}$He+Au in five centrality bins and for inelastic collisions at $\sqrt{s}$ = 200 GeV. The right boxes are the uncertainties of the $N_{coll}$ collisions from the Glauber model, while the left box represents the overall normalization uncertainty from p+p collisions

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations. Centrality vs. the number of collisions is tabulated here.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations. Centrality vs. Average $R_{xA}$ is tabulated here.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations. Centrality vs. the number of collisions is tabulated here.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations. Centrality vs. Average $R_{xA}$ is tabulated here.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations. Centrality vs. the number of collisions per projectile participant is tabulated here.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations. Centrality vs. Average $R_{xA}$ is tabulated here.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations. Centrality vs. the number of collisions per projectile participant is tabulated here.

Average $R_{xA}$ in two different $p_T$ regions versus the number of collisions (panels a,b) and the number of collisions per projectile participant (panels c,d). The tilted error bars represent the anti-correlated uncertainty on the y and x-axis due to the Ncoll calculations. Centrality vs. Average $R_{xA}$ is tabulated here.

$R_{xA}$ for inelastic collisions compared to three different nuclear PDF calculations and their uncertainties. The data points include the statistical and systematical uncertainties. Relevant data has been tabulated in Figure 7.

The upper panel (a) shows the <$R_{xA}$> above $p_T$ =8 Gev/c as a function $N_{coll}$/$N_{proj}$. The data are compared to predictions from [49] for the consequences of high-$x$ nucleon size fluctuations. The lower panels show the $R_{xA}$ as a function of $p_T$ for most central on left (b) and most peripheral on right panel (c) collisions. Panel (a) has been tabulated in Figure 10 and Panel (b) and (c) in Figure 9.

The upper panel (a) shows the <$R_{xA}$> above $p_T$ =8 Gev/c as a function $N_{coll}$/$N_{proj}$. The data are compared to predictions from [49] for the consequences of high-$x$ nucleon size fluctuations. The lower panels show the $R_{xA}$ as a function of $p_T$ for most central on left (b) and most peripheral on right panel (c) collisions. Panel (a) has been tabulated in Figure 10 and Panel (b) and (c) in Figure 9. Centrality vs. the number of collisions per projectile participant in panel (a) is tabulated here.

The upper panel (a) shows the <$R_{xA}$> above $p_T$ =8 Gev/c as a function $N_{coll}$/$N_{proj}$. The data are compared to predictions from [49] for the consequences of high-$x$ nucleon size fluctuations. The lower panels show the $R_{xA}$ as a function of $p_T$ for most central on left (b) and most peripheral on right panel (c) collisions. Panel (a) has been tabulated in Figure 10 and Panel (b) and (c) in Figure 9. Centrality vs. Average $R_{xA}$ in panel (a) is tabulated here.

The upper panel (a) shows the <$R_{xA}$> above $p_T$ =8 Gev/c as a function $N_{coll}$/$N_{proj}$. The data are compared to predictions from [49] for the consequences of high-$x$ nucleon size fluctuations. The lower panels show the $R_{xA}$ as a function of $p_T$ for most central on left (b) and most peripheral on right panel (c) collisions. Panel (a) has been tabulated in Figure 10 and Panel (b) and (c) in Figure 9. Panel(b) and panel(c) are tabulated here.

Integrated yields for 1--2 GeV/c in panel (a) and 2--3 GeV/c in panel (b) as a function of charged particle multiplicity density at mid rapidity.

Integrated yields for 1--2 GeV/c in panel (a) and 2--3 GeV/c in panel (b) as a function of charged particle multiplicity density at mid rapidity. Centrality vs. charged particle multiplicity density at mid rapidity is tabulated here.

Integrated yields for 1--2 GeV/c in panel (a) and 2--3 GeV/c in panel (b) as a function of charged particle multiplicity density at mid rapidity. Centrality vs. integrated yield is tabulated here.

Integrated yields for 1--2 GeV/c in panel (a) and 2--3 GeV/c in panel (b) as a function of charged particle multiplicity density at mid rapidity.

Integrated yields for 1--2 GeV/c in panel (a) and 2--3 GeV/c in panel (b) as a function of charged particle multiplicity density at mid rapidity. Centrality vs. charged particle multiplicity density at mid rapidity is tabulated here.

Integrated yields for 1--2 GeV/c in panel (a) and 2--3 GeV/c in panel (b) as a function of charged particle multiplicity density at mid rapidity. Centrality vs. integrated yield is tabulated here.

Measured DPS effective cross section

pp -> J/psi J/psi J/psi X cross section

$pp \rightarrow J/\psi J/\psi J/\psi X~$ fiducial cross section

Summary of the systematic uncertainties on the ratio RMN.

Summary of the systematic uncertainties on the ratio Rincl_veto.

Summary of the systematic uncertainties on the ratio RMN_veto.

Differential cross section dσincl/d∆y for inclusive dijet production together with predictions of different MC models.

Differential cross section dσMN/d∆y for Mueller-Navelet dijet production together with predictions of different MC models.

Ratio Rincl of the cross sections for inclusive to ”exclusive” dijet production together with predictions of different MC models.

Ratio Rincl_veto of the cross sections for inclusive to ”exclusive” with veto dijet production together with predictions of different MC models.

Ratio RMN of the cross sections for Mueller-Navelet to ”exclusive” dijet production together with predicgtion of different MC models.

Ratio RMN_veto of the cross sections for Mueller-Navelet to ”exclusive” with veto dijet production together with predictions of different MC models.

Summary of the systematic uncertainties on the cross section dσincl/d∆y.

Summary of the systematic uncertainties on the cross section dσMN/d∆y.

Summary of the systematic uncertainties on the ratio Rincl.

Summary of the systematic uncertainties on the ratio RMN.

Summary of the systematic uncertainties on the ratio Rincl_veto.

Summary of the systematic uncertainties on the ratio RMN_veto.

Differential cross section dσincl/d∆y for inclusive dijet production together with predictions of different MC models.

Differential cross section dσMN/d∆y for Mueller-Navelet dijet production together with predictions of different MC models.

Ratio Rincl of the cross sections for inclusive to ”exclusive” dijet production together with predictions of different MC models.

Ratio Rincl_veto of the cross sections for inclusive to ”exclusive” with veto dijet production together with predictions of different MC models.

Ratio RMN of the cross sections for Mueller-Navelet to ”exclusive” dijet production together with predicgtion of different MC models.

Ratio RMN_veto of the cross sections for Mueller-Navelet to ”exclusive” with veto dijet production together with predictions of different MC models.

Simulated signal, total background, and observed data in the signal category with exactly 1 b jet and 4 or more jets for the three data-taking years combined. For the uncertainty on the signal and background, both the total (systematic+statistical) and statistical uncertainties are provided. The uncertainty on the data is the (statistical) Poisson uncertainty. Note that this is the prefit version.

Simulated signal, total background, and observed data in the signal category with 2 or more b jets for the three data-taking years combined. For the uncertainty on the signal and background, both the total (systematic+statistical) and statistical uncertainties are provided. The uncertainty on the data is the (statistical) Poisson uncertainty. Note that this is the prefit version.

Simulated signal, total background, and observed data in the signal category with exactly 1 b jet and 2-3 jets for the three data-taking years combined. For the uncertainty on the signal and background, both the total (systematic+statistical) and statistical uncertainties are provided. The uncertainty on the data is the (statistical) Poisson uncertainty. Note that this is the postfit version.

Simulated signal, total background, and observed data in the signal category with exactly 1 b jet and 4 or more jets for the three data-taking years combined. For the uncertainty on the signal and background, both the total (systematic+statistical) and statistical uncertainties are provided. The uncertainty on the data is the (statistical) Poisson uncertainty. Note that this is the postfit version.

Simulated signal, total background, and observed data in the signal category with 2 or more b jets for the three data-taking years combined. For the uncertainty on the signal and background, both the total (systematic+statistical) and statistical uncertainties are provided. The uncertainty on the data is the (statistical) Poisson uncertainty. Note that this is the postfit version.

Absolute differential cross sections as a function of the transverse momentum of the Z boson candidate at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the Z boson candidate at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the Z boson candidate at parton level.

Absolute differential cross sections as a function of the transverse momentum of the Z boson candidate at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the Z boson candidate at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the Z boson candidate at particle level.

Absolute differential cross sections as a function of the transverse momentum of the recoiling jet at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the recoiling jet at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the recoiling jet at particle level.

Absolute differential cross sections as a function of the absolute pseudorapidity of the recoiling jet at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the absolute pseudorapidity of the recoiling jet at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the absolute pseudorapidity of the recoiling jet at particle level.

Absolute differential cross sections as a function of the difference in azimuthal angle of the leptons, associated to the Z boson candidate at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the difference in azimuthal angle of the leptons, associated to the Z boson candidate at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the difference in azimuthal angle of the leptons, associated to the Z boson candidate at parton level.

Absolute differential cross sections as a function of the difference in azimuthal angle of the leptons, associated to the Z boson candidate at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the difference in azimuthal angle of the leptons, associated to the Z boson candidate at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the difference in azimuthal angle of the leptons, associated to the Z boson candidate at particle level.

Absolute differential cross sections as a function of the transverse momentum of the leptons, associated to the top candidate at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the leptons, associated to the top candidate at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the leptons, associated to the top candidate at parton level.

Absolute differential cross sections as a function of the transverse momentum of the leptons, associated to the top candidate at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the leptons, associated to the top candidate at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the leptons, associated to the top candidate at particle level.

Absolute differential cross sections as a function of the invariant mass of the three-lepton system at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the invariant mass of the three-lepton system at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the invariant mass of the three-lepton system at parton level.

Absolute differential cross sections as a function of the invariant mass of the three-lepton system at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the invariant mass of the three-lepton system at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the invariant mass of the three-lepton system at particle level.

Absolute differential cross sections as a function of the transverse momentum of the top candidate at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the top candidate at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the top candidate at parton level.

Absolute differential cross sections as a function of the transverse momentum of the top candidate at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the top candidate at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the transverse momentum of the top candidate at particle level.

Absolute differential cross sections as a function of the invariant mass of the top-Z system at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the invariant mass of the top-Z system at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the invariant mass of the top-Z system at parton level.

Absolute differential cross sections as a function of the invariant mass of the top-Z system at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the invariant mass of the top-Z system at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the invariant mass of the top-Z system at particle level.

Absolute differential cross sections as a function of the cosine of the top polarization angle, measured in respect to the spectator quark at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the cosine of the top polarization angle, measured in respect to the spectator quark at parton level.

Covariance matrix for the measurement of the differential cross sections as a function of the cosine of the top polarization angle, measured in respect to the spectator quark at parton level.

Absolute differential cross sections as a function of the cosine of the top polarization angle, measured in respect to the recoiling jet at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the cosine of the top polarization angle, measured in respect to the recoiling jet at particle level.

Covariance matrix for the measurement of the differential cross sections as a function of the cosine of the top polarization angle, measured in respect to the recoiling jet at particle level.

Normalized differential cross sections as a function of the transverse momentum of the Z boson candidate at parton level.

Normalized differential cross sections as a function of the transverse momentum of the Z boson candidate at particle level.

Normalized differential cross sections as a function of the transverse momentum of the recoiling jet at parton level.

Normalized differential cross sections as a function of the absolute pseudorapidity of the recoiling jet at particle level.

Normalized differential cross sections as a function of the difference in azimuthal angle of the leptons, associated to the Z boson candidate at parton level.

Normalized differential cross sections as a function of the difference in azimuthal angle of the leptons, associated to the Z boson candidate at particle level.

Normalized differential cross sections as a function of the transverse momentum of the leptons, associated to the top candidate at parton level.

Normalized differential cross sections as a function of the transverse momentum of the leptons, associated to the top candidate at particle level.

Normalized differential cross sections as a function of the invariant mass of the three-lepton system at parton level.

Normalized differential cross sections as a function of the invariant mass of the three-lepton system at particle level.

Normalized differential cross sections as a function of the transverse momentum of the top candidate at parton level.

Normalized differential cross sections as a function of the transverse momentum of the top candidate at particle level.

Normalized differential cross sections as a function of the invariant mass of the top-Z system at parton level.

Normalized differential cross sections as a function of the invariant mass of the top-Z system at particle level.

Normalized differential cross sections as a function of the cosine of the top polarization angle, measured in respect to the spectator quark at parton level.

Normalized differential cross sections as a function of the cosine of the top polarization angle, measured in respect to the recoiling jet at particle level.

Likelihood scan of the top quark spin asymmetry.

The shape calibration SF values as a function of CvsL and CvsB for DeepJet-based c taggers for c jets

The shape calibration SF values as a function of CvsL and CvsB for DeepJet-based c taggers for b jets

The shape calibration SF values as a function of CvsL and CvsB for DeepJet-based c taggers for light-flavour jets