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 correlation functions (corrected for nonuniform detector efficiency in $\phi$; not corrected for the absolute detection efficiency) vs. azimuthal angle difference between forward ($2.6<\eta<4.0$) $\pi^{0}$s in $p$+$p$ collisions at $\sqrt{s_{\mathrm{_{NN}}}}=200$ GeV at high $p_{T}$ ($p^{trig}_{T}$=2.5-3 GeV/c, $p^{asso}_{T}$=2-2.5 GeV/c)

The correlation functions (corrected for nonuniform detector efficiency in $\phi$; not corrected for the absolute detection efficiency) vs. azimuthal angle difference between forward ($2.6<\eta<4.0$) $\pi^{0}$s in $p$+Al collisions at $\sqrt{s_{\mathrm{_{NN}}}}=200$ GeV at high $p_{T}$ ($p^{trig}_{T}$=2.5-3 GeV/c, $p^{asso}_{T}$=2-2.5 GeV/c)

The correlation functions (corrected for nonuniform detector efficiency in $\phi$; not corrected for the absolute detection efficiency) vs. azimuthal angle difference between forward ($2.6<\eta<4.0$) $\pi^{0}$s in $p$+$Au collisions at $\sqrt{s_{\mathrm{_{NN}}}}=200$ GeV at high $p_{T}$ ($p^{trig}_{T}$=2.5-3 GeV/c, $p^{asso}_{T}$=2-2.5 GeV/c)

Relative area of MinBias $pAu/pp$ of back-to-back di-$\pi^0$ correlations at forward pseudorapidities ($2.6<\eta<4.0$) from data

Relative area of MinBias $pAl/pp$ of back-to-back di-$\pi^0$ correlations at forward pseudorapidities ($2.6<\eta<4.0$) from data

Relative area of $pAu/pp$ at b=0 of back-to-back di-$\pi^0$ correlations at forward pseudorapidities ($2.6<\eta<4.0$) from prediction

Relative width of MinBias $pAu/pp$ of back-to-back di-$\pi^0$ correlations at forward pseudorapidities ($2.6<\eta<4.0$) from data

Relative width of MinBias $pAl/pp$ of back-to-back di-$\pi^0$ correlations at forward pseudorapidities ($2.6<\eta<4.0$) from data

Relative pedestal of MinBias $pAu/pp$ of back-to-back di-$\pi^0$ correlations at forward pseudorapidities ($2.6<\eta<4.0$) from data

Relative pedestal of MinBias $pAl/pp$ of back-to-back di-$\pi^0$ correlations at forward pseudorapidities ($2.6<\eta<4.0$) from data

Relative area of MinBias $pp/pp$ of back-to-back di-$\pi^0$ correlations at forward pseudorapidities ($2.6<\eta<4.0$), the ratio is unity, error is zero

Relative area of MinBias $pA/pp$ of back-to-back di-$\pi^0$ correlations at forward pseudorapidities ($2.6<\eta<4.0$) from data

The efficiency for simulated VBF events to pass each analysis cut.

The efficiency for simulated VH events to pass each analysis cut.

The efficiency for simulated ttH events to pass each analysis cut.

The fractional contribution of each production mode to a given analysis region around the Higgs boson peak, defined as 105 < mJ < 140 GeV. The fraction is given with respect to the total signal yield in the analysis region in question.

Signal acceptance times efficiency within the fiducial volume used in the fiducial region.

Signal acceptance times efficiency for the STXS volumes in the differential measurement. Along with the pTH requirements shown, |yH | < 2 is required. For events with pTH < 300 GeV, the acceptance times efficiency is less than 0.1 × 10−2.

Event yields and associated uncertainties after the global likelihood fit in the pT(H) > 450 GeV fiducial region. The total background yield can differ from the sum of the individual components due to rounding.

Event yields and associated uncertainties after the global likelihood fit of the differential regions. The total background yield can differ from the sum of the individual components due to rounding. The five STXS volumes are labeled pT0–pT4 corresponding to pTH < 300 GeV, 300 – 450 GeV, 450 – 650 GeV, 650 – 1000 GeV, and > 1000 GeV, respectively. The pT0 event yield is constrained to its SM value within the theoretical and experimental uncertainties and free parameters act independently on the remaining four volumes.

The JER for R = 0.2 jets in Pb+Pb collisions as a function of $2|\Psi_2-\phi^{reco}|$ for centrality selections of 0-5%, 5-10%, 10-20%, 20-40% and 40-60%.

The systematic uncertainties in v2 for 20-40% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v2 for 5-10% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v3 for 20-40% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v3 for 5-10% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v4 for 20-40% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v4 for 5-10% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v2 for pT = 71--398 GeV jets as a function of centrality.

The systematic uncertainties in v3 for pT = 71--398 GeV jets as a function of centrality.

The systematic uncertainties in v4 for pT = 71--398 GeV jets as a function of centrality.

Angular distribution of jets as a function of the observed psi2 plane for jets with 71 < pT < 79 GeV in the 10-20% centrality bin.

Angular distribution of jets as a function of the observed psi3 plane for jets with 71 < pT < 79 GeV in the 10-20% centrality bin.

Angular distribution of jets as a function of the observed psi4 plane for jets with 71 < pT < 79 GeV in the 10-20% centrality bin.

The v2 values for R = 0.2 jets as a function of centrality for jets in several pT ranges.

The v2 values for R = 0.2 jets as a function of pT for 0-5%, 5-10%, and 20-40% centrality collisions.

The v2, v3, and v4 as a function of centrality for jets with pT = 71-398 GeV.

The v3 values for R = 0.2 jets as a function of centrality for jets in several pT ranges.

The v4 values for R = 0.2 jets as a function of centrality for jets in several pT ranges.

The v2 as a function of pT for jets in 10-20% centrality collisions.

The v3 as a function of pT for jets in 10-20% centrality collisions.

The v2 as a function of pT for jets in 20-40% centrality collisions.

The v3 as a function of pT for jets in 20-40% centrality collisions.

The v2 for jets in 10-20% centrality collisions.

The v3 for jets in 10-20% centrality collisions.

R2max as a function of pT (filled circles). Also shown is 1 - 4v2/(1+2v2).

R3max as a function of pT (filled circles). Also shown is 1 - 4v3/(1+2v3).

The systematic uncertainties in v2 for 40-60% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v3 for 40-60% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v4 for 40-60% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v2 for 10-20% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v3 for 10-20% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v4 for 10-20% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v2 for 0-5% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v3 for 0-5% centrality Pb+Pb collisions as a function of pT.

The systematic uncertainties in v4 for 0-5% centrality Pb+Pb collisions as a function of pT.

The v2 as a function of pT for jets in 40-60% centrality collisions.

The v2 as a function of pT for jets in 5-10% centrality collisions.

The v2 as a function of pT for jets in 0-5% centrality collisions.

The v3 as a function of pT for jets in 40-60% centrality collisions.

The v3 as a function of pT for jets in 5-10% centrality collisions.

The v3 as a function of pT for jets in 0-5% centrality collisions.

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