Transverse-momentum spectra ratios of $\mathrm{D^0}$ hadrons measured in pp collisions at $\sqrt{{s}} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidityto the INEL>0 event class.

Transverse-momentum spectra ratios of $\mathrm{D_s^+}$ hadrons measured in pp collisions at $\sqrt{{s}} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidityto the INEL>0 event class.

Transverse-momentum spectra ratios of $\mathrm{\Lambda_c^+}$ hadrons measured in pp collisions at $\sqrt{{s}} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidityto the INEL>0 event class.

The $\mathrm{D_s^+ / D^0}$ ratio measured in pp collisions at $\sqrt{s} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidity

The $\mathrm{D_s^+ / D^0}$ ratio measured in pp collisions at $\sqrt{s} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidity

The $\mathrm{\Lambda_c^+ / D^0}$ ratio measured in pp collisions at $\sqrt{s} = 13$~TeV for different multiplicity classes selected with the V0M estimator at forward rapidity

The $\mathrm{\Lambda_c^+ / D^0}$ ratio measured in pp collisions at $\sqrt{s} = 13$~TeV for different multiplicity classes selected with the V0M estimator at forward rapidity

The extrapolated $p_\mathrm{T}$-integrated $\mathrm{\Lambda_c^+ / D^0}$ ratio measured as a function of $\langle \mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta \rangle$ in pp collisions at $\sqrt{{s}} = 13$~TeV

Transverse-momentum spectra of $\mathrm{D^0}$ hadrons measured in pp collisions at $\sqrt{{s}} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidity.

Transverse-momentum spectra of $\mathrm{D_s^+}$ hadrons measured in pp collisions at $\sqrt{{s}} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidity.

Transverse-momentum spectra of $\mathrm{\Lambda_c^+}$ hadrons measured in pp collisions at $\sqrt{{s}} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidity.

Transverse-momentum spectra ratios of $\mathrm{D^0}$ hadrons measured in pp collisions at $\sqrt{{s}} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidityto the INEL>0 event class.

Transverse-momentum spectra ratios of $\mathrm{D_s^+}$ hadrons measured in pp collisions at $\sqrt{{s}} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidityto the INEL>0 event class.

Transverse-momentum spectra ratios of $\mathrm{\Lambda_c^+}$ hadrons measured in pp collisions at $\sqrt{{s}} = 13$~TeV for different multiplicity classes selected with the $N_\mathrm{trkl}$ estimator at midrapidityto the INEL>0 event class.

The visible $p_\mathrm{T}$-integrated $\mathrm{D_s^+ / D^0}$ ratio measured as a function of $\langle \mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta \rangle$ in pp collisions at $\sqrt{{s}} = 13$~TeV

The visible $p_\mathrm{T}$-integrated $\mathrm{\Lambda_c^+ / D^0}$ ratio measured as a function of $\langle \mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta \rangle$ in pp collisions at $\sqrt{{s}} = 13$~TeV

The extrapolated $p_\mathrm{T}$-integrated $\mathrm{D_s^+ / D^0}$ ratio measured as a function of $\langle \mathrm{d}N_\mathrm{ch}/\mathrm{d}\eta \rangle$ in pp collisions at $\sqrt{{s}} = 13$~TeV

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 measured inclusive jet zg spectrum

The measured inclusive jet Rg spectrum

The measured inclusive jet M/E spectrum

The measured inclusive jet Mg/E spectrum

The observed exclusion contours as a function of (m(med), m($\chi$)), with Vector mediator)

The expected exclusion contours as a function of (m(a), tan($\beta$)), with sin($\theta$) = 0.35)

The observed exclusion contours as a function of (m(a), tan($\beta$)), with sin($\theta$) = 0.35)

The expected exclusion contours as a function of (m(a), tan($\beta$)), with sin($\theta$) = 0.7)

The observed exclusion contours as a function of (m(a), tan($\beta$)), with sin($\theta$) = 0.7)

The expected exclusion contours as a function of (m(H), tan($\beta$)), with sin($\theta$) = 0.35)

The observed exclusion contours as a function of (m(H), tan($\beta$)), with sin($\theta$) = 0.35)

The expected exclusion contours as a function of (m(H), tan($\beta$)), with sin($\theta$) = 0.7)

The observed exclusion contours as a function of (m(H), tan($\beta$)), with sin($\theta$) = 0.7)

The expected exclusion contours as a function of (m(a), m(H)), with sin($\theta$) = 0.35)

The observed exclusion contours as a function of (m(a), m(H)), with sin($\theta$) = 0.35)

The expected exclusion contours as a function of (m(a), m(H)), with sin($\theta$) = 0.7)

The observed exclusion contours as a function of (m(a), m(H)), with sin($\theta$) = 0.7)

Expected lower limit on signal strength at 95% CL as a function of sin($\theta$), with m(a) = 200 GeV, m(H) = 600 GeV.

Observed lower limit on signal strength at 95% CL as a function of sin($\theta$), with m(a) = 200 GeV, m(H) = 600 GeV.

Expected lower limit on signal strength at 95% CL as a function of sin($\theta$), with m(a) = 350 GeV, m(H) = 1000 GeV.

Observed lower limit on signal strength at 95% CL as a function of sin($\theta$), with m(a) = 350 GeV, m(H) = 1000 GeV.

Observed lower limit on WIMP-nucleon cross section at 90% CL as a function of m(WIMP), assuming Higgs-portal scenario with Scalar WIMP.

Observed lower limit on WIMP-nucleon cross section at 90% CL as a function of m(WIMP), assuming Higgs-portal scenario with Majorana WIMP.

Observed lower limit on the spin-dependent WIMP–proton scattering cross-section.

Observed lower limit on the spin-independent WIMP–nucleon scattering cross-section.

Cutflow of unweighted and weighted events of $ZH$ signals in the electron channel.

Cutflow of unweighted and weighted events of $ZH$ signals in the muon channel.

Cutflow of unweighted and weighted events of DM signals (simplified DM axial-vector with m(med) = 900 GeV and m($\chi$) = 40 GeV, 2HDM+$a$ signal with m(A) = 600 GeV, m(a) = 400 GeV, tan($\beta$) = 1.0 and m($\chi$) = 10 GeV) in the electron channel.

Cutflow of unweighted and weighted events of DM signals (simplified DM axial-vector with m(med) = 900 GeV and m($\chi$) = 40 GeV, 2HDM+$a$ signal with m(A) = 600 GeV, m(a) = 400 GeV, tan($\beta$) = 1.0 and m($\chi$) = 10 GeV) in the muon channel.

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.

Pre-fit distribution of the reconstructed Higgs boson candidate $p_T^H$ for the dilepton $SR^{\geq 4j}_{\geq 4b}$ signal region. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations, except for the uncertainty in the $k({t\bar {t}+{\geq }1b})$ normalisation factor which is not defined pre-fit. The last bin includes the overflow.

Pre-fit distribution of the reconstructed Higgs boson candidate $p_T^H$ for the single-lepton resolved $SR^{\geq 6j}_{\geq 4b}$ signal region. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations, except for the uncertainty in the $k({t\bar {t}+{\geq }1b})$ normalisation factor which is not defined pre-fit. The last bin includes the overflow.

Pre-fit distribution of the reconstructed Higgs boson candidate $p_T^H$ for the single-lepton boosted ${{SR}_{{boosted}}}$ signal region. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations, except for the uncertainty in the $k({t\bar {t}+{\geq }1b})$ normalisation factor which is not defined pre-fit. The last bin includes the overflow.

Comparison of predicted and observed event yields in each of the control and signal regions in the dilepton channel after the fit to the data. The uncertainty band includes all uncertainties and their correlations.

Comparison of predicted and observed event yields in each of the control and signal regions in the single-lepton channels after the fit to the data. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the dilepton SRs after the inclusive fit to the data for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the dilepton SRs after the inclusive fit to the data for $120\le p_T^H<200$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the dilepton SRs after the inclusive fit to the data for $200\le p_T^H<300$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the dilepton SRs after the inclusive fit to the data for $p_{{T}}^{H}\ge 300$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the single-lepton resolved SRs after the inclusive fit to the data for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the single-lepton resolved SRs after the inclusive fit to the data for $120\le p_T^H<200$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the single-lepton resolved SRs after the inclusive fit to the data for $200\le p_T^H<300$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the single-lepton resolved SRs after the inclusive fit to the data for $300\le p_T^H<450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the single-lepton resolved SRs after the inclusive fit to the data for $p_{{T}}^{H}\ge 450$ GeV (yield only). The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the single-lepton boosted SRs after the inclusive fit to the data for $300\le p_T^H<450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the BDT discriminant in the single-lepton boosted SRs after the inclusive fit to the data for $p_{{T}}^{H}\ge 450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for ${\Delta R^{{avg}}_{bb}}$ after the inclusive fit to the data in the single-lepton $CR^{5j}_{{\geq}4b\ lo}$ control region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Comparison between data and prediction for ${\Delta R^{{avg}}_{bb}}$ after the inclusive fit to the data in the single-lepton $CR^{5j}_{{\geq}4b\ hi}$ control region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Post-fit yields of signal ($S$) and total background ($B$) as a function of $\log (S/B)$, compared with data. Final-discriminant bins in all dilepton and single-lepton analysis regions are combined into bins of $\log (S/B)$, with the signal normalised to the SM prediction used for the computation of $\log (S/B)$. The signal is then shown normalised to the best-fit value and the SM prediction. The lower frame reports the ratio of data to background, and this is compared with the expected ${t\bar {t}H}$-signal-plus-background yield divided by the background-only yield for the best-fit signal strength (solid red line) and the SM prediction (dashed orange line).

Comparison between data and prediction for the reconstruction BDT score for the Higgs boson candidate identified using Higgs boson information, after the inclusive fit to the data in the dilepton resolved channel for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the average $\Delta \eta $ between $b$-tagged jets, after the inclusive fit to the data in the dilepton resolved channel for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the likelihood discriminant, after the inclusive fit to the data in the single-lepton resolved channel for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the average $\Delta R$ for all possible combinations of $b$-tagged jet pairs, after the inclusive fit to the data in the single-lepton resolved channel for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the DNN $P(H)$ output for the Higgs boson candidate after the inclusive fit to the data in the single-lepton boosted channel for $300\le p_T^H<450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the DNN $P(H)$ output for the Higgs boson candidate after the inclusive fit to the data in the single-lepton boosted channel for $p_{{T}}^{H}\ge 450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Post-fit distribution of the reconstructed Higgs boson candidate mass for the dilepton $SR^{\geq 4j}_{\geq 4b}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Post-fit distribution of the reconstructed Higgs boson candidate mass for the single-lepton resolved $SR^{\geq 6j}_{\geq 4b}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Post-fit distribution of the reconstructed Higgs boson candidate mass for the single-lepton boosted ${{SR}_{{boosted}}}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Fitted values of the ${t\bar {t}H}$ signal strength parameter in the individual channels and in the inclusive signal-strength measurement.

Ranking of the 20 nuisance parameters with the largest post-fit impact on $\mu $ in the fit. Nuisance parameters corresponding to statistical uncertainties in the simulated event samples are not included. The empty blue rectangles correspond to the pre-fit impact on $\mu $ and the filled blue ones to the post-fit impact on $\mu $, both referring to the upper scale. The impact of each nuisance parameter, $\Delta \mu $, is computed by comparing the nominal best-fit value of $\mu $ with the result of the fit when fixing the considered nuisance parameter to its best-fit value, $\hat{\theta }$, shifted by its pre-fit (post-fit) uncertainties $\pm \Delta \theta $ ($\pm \Delta \hat{\theta }$). The black points show the pulls of the nuisance parameters relative to their nominal values, $\theta _0$. These pulls and their relative post-fit errors, $\Delta \hat{\theta }/\Delta \theta $, refer to the lower scale. The `ljets' (`dilep') label refers to the single-lepton (dilepton) channel.

Pre-fit distribution of the number of jets in the dilepton $SR^{\geq 4j}_{\geq 4b}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the Standard Model expectation. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations, except the uncertainty in the $k({t\bar {t}+{\geq }1b})$ normalisation factor that is not defined pre-fit.

Pre-fit distribution of the number of jets in the single-lepton resolved $SR^{\geq 6j}_{\geq 4b}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the Standard Model expectation. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations, except the uncertainty in the $k({t\bar {t}+{\geq }1b})$ normalisation factor that is not defined pre-fit.

Pre-fit distribution of the number of jets in the single-lepton boosted ${{SR}_{{boosted}}}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the Standard Model expectation. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations, except the uncertainty in the $k({t\bar {t}+{\geq }1b})$ normalisation factor that is not defined pre-fit.

Post-fit distribution of the number of jets in the dilepton $SR^{\geq 4j}_{\geq 4b}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Post-fit distribution of the number of jets in the single-lepton resolved $SR^{\geq 6j}_{\geq 4b}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Post-fit distribution of the number of jets in the single-lepton boosted ${{SR}_{{boosted}}}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Post-fit distribution of the reconstructed Higgs boson candidate $p_T^H$ for the dilepton $SR^{\geq 4j}_{\geq 4b}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The last bin includes the overflow.

Post-fit distribution of the reconstructed Higgs boson candidate $p_T^H$ for the single-lepton resolved $SR^{\geq 6j}_{\geq 4b}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The last bin includes the overflow.

Post-fit distribution of the reconstructed Higgs boson candidate $p_T^H$ for the single-lepton boosted ${{SR}_{{boosted}}}$ signal region. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The last bin includes the overflow.

Signal-strength measurements in the individual STXS ${\hat{p}_{{T}}^{H}}$ bins, as well as the inclusive signal strength.

95% CL simplified template cross-section upper limits in the individual STXS ${\hat{p}_{{T}}^{H}}$ bins, as well as the inclusive limit. The observed limits are shown (solid black lines), together with the expected limits both in the background-only hypothesis (dotted black lines) and in the SM hypothesis (dotted red lines). In the case of the expected limits in the background-only hypothesis, one- and two-standard-deviation uncertainty bands are also shown. The hatched uncertainty bands correspond to the theory uncertainty in the fiducial cross-section prediction in each bin.

The ratios $S/B$ (black solid line, referring to the vertical axis on the left) and $S/\sqrt{B}$ (red dashed line, referring to the vertical axis on the right) for each category in the inclusive analysis in the dilepton channel (left) and in the single-lepton channels (right), where $S$ ($B$) is the number of selected signal (background) events predicted by the simulation and normalised to a luminosity of 139 fb$^{-1}$ .

Comparison between data and prediction for the $\Delta R$ between the Higgs candidate and the ${t\bar {t}}$ candidate system, after the inclusive fit to the data in the dilepton resolved channel for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the number of $b$-tagged jet pairs with an invariant mass within 30 GeV of 125 GeV, after the inclusive fit to the data in the dilepton resolved channel for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the reconstruction BDT score for the Higgs boson candidate identified using Higgs boson information, after the inclusive fit to the data in the single-lepton resolved channel for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the $\Delta R$ between the two highest ${p_{{T}}}$ $b$-tagged jets, after the inclusive fit to the data in the single-lepton resolved channel for $0\le p_T^H<120$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations.

Comparison between data and prediction for the sum of $b$-tagging discriminants of jets from Higgs, hadronic top and leptonic top candidates, after the inclusive fit to the data in the single-lepton boosted channel for $300\le p_T^H<450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Comparison between data and prediction for the sum of $b$-tagging discriminants of jets from Higgs, hadronic top and leptonic top candidates, after the inclusive fit to the data in the single-lepton boosted channel for $p_{{T}}^{H}\ge 450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Comparison between data and prediction for the hadronic top candidate invariant mass, after the inclusive fit to the data in the single-lepton boosted channel for $300\le p_T^H<450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Comparison between data and prediction for the hadronic top candidate invariant mass, after the inclusive fit to the data in the single-lepton boosted channel for $p_{{T}}^{H}\ge 450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Comparison between data and prediction for the fraction of the sum of $b$-tagging discriminants of all jets not associated to the Higgs or hadronic top candidates, after the inclusive fit to the data in the single-lepton boosted channel for $300\le p_T^H<450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Comparison between data and prediction for the fraction of the sum of $b$-tagging discriminants of all jets not associated to the Higgs or hadronic top candidates, after the inclusive fit to the data in the single-lepton boosted channel for $p_{{T}}^{H}\ge 450$ GeV. The ${t\bar {t}H}$ signal yield (solid red) is normalised to the fitted $\mu $ value from the inclusive fit. The dashed line shows the ${t\bar {t}H}$ signal distribution normalised to the total background prediction. The uncertainty band includes all uncertainties and their correlations. The first (last) bin includes the underflow (overflow).

Ranking of the 20 nuisance parameters with the largest post-fit impact on $\mu $ in the STXS fit for $0\le {\hat{p}_{{T}}^{H}}<120$ GeV. Nuisance parameters corresponding to statistical uncertainties in the simulated event samples are not included. The empty blue rectangles correspond to the pre-fit impact on $\mu $ and the filled blue ones to the post-fit impact on $\mu $, both referring to the upper scale. The impact of each nuisance parameter, $\Delta \mu $, is computed by comparing the nominal best-fit value of $\mu $ with the result of the fit when fixing the considered nuisance parameter to its best-fit value, $\hat{\theta }$, shifted by its pre-fit (post-fit) uncertainties $\pm \Delta \theta $ ($\pm \Delta \hat{\theta }$). The black points show the pulls of the nuisance parameters relative to their nominal values, $\theta _0$. These pulls and their relative post-fit errors, $\Delta \hat{\theta }/\Delta \theta $, refer to the lower scale. For experimental uncertainties that are decomposed into several independent sources, NP X corresponds to the X$^{th}$ nuisance parameter, ordered by their impact on $\mu $. The `ljets' (`dilep') label refers to the single-lepton (dilepton) channel.

Ranking of the 20 nuisance parameters with the largest post-fit impact on $\mu $ in the STXS fit for $120\le {\hat{p}_{{T}}^{H}}<200$ GeV. Nuisance parameters corresponding to statistical uncertainties in the simulated event samples are not included. The empty blue rectangles correspond to the pre-fit impact on $\mu $ and the filled blue ones to the post-fit impact on $\mu $, both referring to the upper scale. The impact of each nuisance parameter, $\Delta \mu $, is computed by comparing the nominal best-fit value of $\mu $ with the result of the fit when fixing the considered nuisance parameter to its best-fit value, $\hat{\theta }$, shifted by its pre-fit (post-fit) uncertainties $\pm \Delta \theta $ ($\pm \Delta \hat{\theta }$). The black points show the pulls of the nuisance parameters relative to their nominal values, $\theta _0$. These pulls and their relative post-fit errors, $\Delta \hat{\theta }/\Delta \theta $, refer to the lower scale. For experimental uncertainties that are decomposed into several independent sources, NP X corresponds to the X$^{th}$ nuisance parameter, ordered by their impact on $\mu $. The `ljets' (`dilep') label refers to the single-lepton (dilepton) channel.

Ranking of the 20 nuisance parameters with the largest post-fit impact on $\mu $ in the STXS fit for $200\le {\hat{p}_{{T}}^{H}}<300$ GeV. Nuisance parameters corresponding to statistical uncertainties in the simulated event samples are not included. The empty blue rectangles correspond to the pre-fit impact on $\mu $ and the filled blue ones to the post-fit impact on $\mu $, both referring to the upper scale. The impact of each nuisance parameter, $\Delta \mu $, is computed by comparing the nominal best-fit value of $\mu $ with the result of the fit when fixing the considered nuisance parameter to its best-fit value, $\hat{\theta }$, shifted by its pre-fit (post-fit) uncertainties $\pm \Delta \theta $ ($\pm \Delta \hat{\theta }$). The black points show the pulls of the nuisance parameters relative to their nominal values, $\theta _0$. These pulls and their relative post-fit errors, $\Delta \hat{\theta }/\Delta \theta $, refer to the lower scale. For experimental uncertainties that are decomposed into several independent sources, NP X corresponds to the X$^{th}$ nuisance parameter, ordered by their impact on $\mu $. The `ljets' (`dilep') label refers to the single-lepton (dilepton) channel.

Ranking of the 20 nuisance parameters with the largest post-fit impact on $\mu $ in the STXS fit for $300\le {\hat{p}_{{T}}^{H}}<450$ GeV. Nuisance parameters corresponding to statistical uncertainties in the simulated event samples are not included. The empty blue rectangles correspond to the pre-fit impact on $\mu $ and the filled blue ones to the post-fit impact on $\mu $, both referring to the upper scale. The impact of each nuisance parameter, $\Delta \mu $, is computed by comparing the nominal best-fit value of $\mu $ with the result of the fit when fixing the considered nuisance parameter to its best-fit value, $\hat{\theta }$, shifted by its pre-fit (post-fit) uncertainties $\pm \Delta \theta $ ($\pm \Delta \hat{\theta }$). The black points show the pulls of the nuisance parameters relative to their nominal values, $\theta _0$. These pulls and their relative post-fit errors, $\Delta \hat{\theta }/\Delta \theta $, refer to the lower scale. For experimental uncertainties that are decomposed into several independent sources, NP X corresponds to the X$^{th}$ nuisance parameter, ordered by their impact on $\mu $. The `ljets' (`dilep') label refers to the single-lepton (dilepton) channel.

Ranking of the 20 nuisance parameters with the largest post-fit impact on $\mu $ in the STXS fit for ${\hat{p}_{{T}}^{H}}\ge 450$ GeV. Nuisance parameters corresponding to statistical uncertainties in the simulated event samples are not included. The empty blue rectangles correspond to the pre-fit impact on $\mu $ and the filled blue ones to the post-fit impact on $\mu $, both referring to the upper scale. The impact of each nuisance parameter, $\Delta \mu $, is computed by comparing the nominal best-fit value of $\mu $ with the result of the fit when fixing the considered nuisance parameter to its best-fit value, $\hat{\theta }$, shifted by its pre-fit (post-fit) uncertainties $\pm \Delta \theta $ ($\pm \Delta \hat{\theta }$). The black points show the pulls of the nuisance parameters relative to their nominal values, $\theta _0$. These pulls and their relative post-fit errors, $\Delta \hat{\theta }/\Delta \theta $, refer to the lower scale. For experimental uncertainties that are decomposed into several independent sources, NP X corresponds to the X$^{th}$ nuisance parameter, ordered by their impact on $\mu $. The `ljets' (`dilep') label refers to the single-lepton (dilepton) channel.

95% confidence level upper limits on signal-strength measurements in the individual STXS ${\hat{p}_{{T}}^{H}}$ bins, as well as the inclusive signal-strength limit, after the fit used to extract multiple signal-strength parameters. The observed limits are shown (solid black lines), together with the expected limits both in the background-only hypothesis (dotted black lines) and in the SM hypothesis (dotted red lines). In the case of the expected limits in the background-only hypothesis, one- and two-standard-deviation uncertainty bands are also shown.

Post-fit correlation matrix (in percentages) between the $\mu $ values obtained in the STXS bins.

Performance of the Higgs boson reconstruction algorithms. For each row of `truth' ${\hat{p}_{{T}}^{H}}$, the matrix shows (in percentages) the fraction of Higgs boson candidates which are truth-matched to ${b\bar {b}}$ decays, with reconstructed $p_T^H$ in the various bins of the dilepton (left), single lepton resolved (middle) and boosted (right) channels.

Pre-fit event yields in the dilepton signal regions and control regions. All uncertainties are included except the $k({t\bar {t}+{\geq }1b})$ uncertainty that is not defined pre-fit. For the ${t\bar {t}H}$ signal, the pre-fit yield values correspond to the theoretical prediction and corresponding uncertainties. `Other sources' refers to s-channel, t-channel, $tW$, $tWZ$, $tZq$, $Z+$ jets and diboson events.

Post-fit event yields in the dilepton signal regions and control regions, after the inclusive fit in all channels. All uncertainties are included, taking into account correlations. For the ${t\bar {t}H}$ signal, the post-fit yield and uncertainties correspond to those in the inclusive signal-strength measurement. `Other sources' refers to s-channel, t-channel, $tW$, $tWZ$, $tZq$, $Z+$ jets and diboson events.

Pre-fit event yields in the single-lepton resolved and boosted signal regions and control regions. All uncertainties are included except the $k({t\bar {t}+{\geq }1b})$ uncertainty that is not defined pre-fit. For the ${t\bar {t}H}$ signal, the pre-fit yield values correspond to the theoretical prediction and corresponding uncertainties. `Other top sources' refers to s-channel, t-channel, $tWZ$ and $tZq$ events.

Post-fit event yields in the single-lepton resolved and boosted signal regions and control regions, after the inclusive fit in all channels. All uncertainties are included, taking into account correlations. For the ${t\bar {t}H}$ signal, the post-fit yield and uncertainties correspond to those in the inclusive signal-strength measurement. `Other top sources' refers to s-channel, t-channel, $tWZ$ and $tZq$ events.

Breakdown of the contributions to the uncertainties in $\mu$. The contributions from the different sources of uncertainty are evaluated after the fit. The $\Delta \mu $ values are obtained by repeating the fit after having fixed a certain set of nuisance parameters corresponding to a group of systematic uncertainties, and then evaluating $(\Delta \mu)^2$ by subtracting the resulting squared uncertainty of $\mu $ from its squared uncertainty found in the full fit. The same procedure is followed when quoting the effect of the ${t\bar {t}+{\geq }1b}$ normalisation. The total uncertainty is different from the sum in quadrature of the different components due to correlations between nuisance parameters existing in the fit.

Fraction (in percentages) of signal events, after SR and CR selections, originating from $b\bar {b}$, $WW$ and other remaining Higgs boson decay modes in the dilepton channel.

Fraction (in percentages) of signal events, after SR and CR selections, originating from $b\bar {b}$, $WW$ and other remaining Higgs boson decay modes in the single-lepton channels.

Predicted SM ${t\bar {t}H}$ cross-section in each of the five STXS ${\hat{p}_{{T}}^{H}}$ bins and signal acceptance times efficiency (including all event selection criteria) in each STXS bin as well as for the inclusive ${\hat{p}_{{T}}^{H}}$ range.

Number of expected signal events before the fit, after each selection requirement applied to enter the dilepton channel $SR^{\geq 4j}_{\geq 4b}$ region. All ${t\bar {t}H}$ signal events are included, regardless of the $H$ or ${t\bar {t}H}$ decay mode. All object corrections are applied, except for the initial number of events which is calculated using the NLO QCD+EW theoretical prediction. All quoted numbers are rounded to unity. More details on the selection criteria can be found in the text.

Number of expected signal events before the fit, after each selection requirement applied to enter the single-lepton channel resolved $SR^{\geq 6j}_{\geq 4b}$ region. All ${t\bar {t}H}$ signal events are included, regardless of the $H$ or ${t\bar {t}H}$ decay mode. All object corrections are applied, except for the initial number of events which is calculated using the NLO QCD+EW theoretical prediction. All quoted numbers are rounded to unity. More details on the selection criteria can be found in the text.

Number of expected signal events before the fit, after each selection requirement applied to enter the single-lepton channel boosted $SR_{boosted}$ region. All ${t\bar {t}H}$ signal events are included, regardless of the $H$ or ${t\bar {t}H}$ decay mode. All object corrections are applied, except for the initial number of events which is calculated using the NLO QCD+EW theoretical prediction. All quoted numbers are rounded to unity. More details on the selection criteria can be found in the text.

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.

Centrality dependence of $\rho\left(v_{2}^{2}, v_{3}^{2}, [p_{\rm T}] \right)$ in Pb--Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV

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

Exclusion limit for BrHZX_BrXee

Exclusion limit for BrHZX_BrXmumu

Exclusion limit for BrHZX_BrXll

Exclusion limit for cah

Exclusion limit for cZh

Exclusion limit for kappa

NuE background+LEE fractional covariance matrix after the 1mu1p constraint from arXiv:2110.14080

NuE background fractional covariance matrix before the 1mu1p constraint from arXiv:2110.14080

NuE background+LEE fractional covariance matrix before the 1mu1p constraint from arXiv:2110.14080

NuE simulation from arXiv:2110.14080

Covariance matrix of the $\nu_\mu$CC inclusive total cross section per nucleon in neutrino energy bins.

Covariance matrix of the $\nu_\mu$CC inclusive differential cross section per nucleon in muon energy bins.

Covariance matrix of the $\nu_\mu$CC inclusive differential cross section per nucleon in hadronic energy transfer bins.

Additional smearing matrix of the $\nu_\mu$CC inclusive total cross section per nucleon in neutrino energy bins.

Additional smearing matrix of the $\nu_\mu$CC inclusive differential cross section per nucleon in muon energy bins.

Additional smearing matrix of the $\nu_\mu$CC inclusive total cross section per nucleon in neutrino energy bins.

$\nu_e$ CC FC covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0), the standard prediction with no low energy excess. This has been constrained by the $\nu_e$CC PC, $\nu_\mu$CC FC, $\nu_\mu$CC PC, $\nu_\mu$CC $\pi^0$ FC, $\nu_\mu$CC $\pi^0$ PC, and NC $\pi^0$ channels under a LEE(x=0) hypothesis. The 1-26th bins/rows/columns correspond to the 26 bins of reconstructed neutrino energy in the $\nu_e$CC FC channel.

$\nu_e$ CC FC covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0), the standard prediction with no low energy excess. This has been constrained by the $\nu_e$CC PC, $\nu_\mu$CC FC, $\nu_\mu$CC PC, $\nu_\mu$CC $\pi^0$ FC, $\nu_\mu$CC $\pi^0$ PC, and NC $\pi^0$ channels under a LEE(x=0) hypothesis. The 1-26th bins/rows/columns correspond to the 26 bins of reconstructed neutrino energy in the $\nu_e$CC FC channel.

$\nu_e$ CC FC covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0), the standard prediction with no low energy excess. This has been constrained by the $\nu_e$CC PC, $\nu_\mu$CC FC, $\nu_\mu$CC PC, $\nu_\mu$CC $\pi^0$ FC, $\nu_\mu$CC $\pi^0$ PC, and NC $\pi^0$ channels under a LEE(x=0) hypothesis. The 1-26th bins/rows/columns correspond to the 26 bins of reconstructed neutrino energy in the $\nu_e$CC FC channel.

Fully contained $\nu_e$CC signal efficiency as a function of true neutrino energy. Each bin shows the fraction of Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which are selected in this channel.

Fully contained $\nu_e$CC signal efficiency as a function of true neutrino energy. Each bin shows the fraction of Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which are selected in this channel.

Fully contained $\nu_e$CC signal efficiency as a function of true neutrino energy. Each bin shows the fraction of Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which are selected in this channel.

Partially contained $\nu_e$CC signal efficiency as a function of true neutrino energy. Each bin shows the fraction of Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which are selected in this channel.

Partially contained $\nu_e$CC signal efficiency as a function of true neutrino energy. Each bin shows the fraction of Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which are selected in this channel.

Partially contained $\nu_e$CC signal efficiency as a function of true neutrino energy. Each bin shows the fraction of Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which are selected in this channel.

Fully contained $\nu_e$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of fully contained Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Fully contained $\nu_e$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of selected fully contained Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Fully contained $\nu_e$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of selected fully contained Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Partially contained $\nu_e$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of partially contained Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Partially contained $\nu_e$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of selected partially contained Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Partially contained $\nu_e$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of selected partially contained Monte-Carlo $\nu_e$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Fully contained $\nu_e$CC data, signal, background, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_\mu$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining intrinsic $\nu_e$CC events. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Fully contained $\nu_\mu$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of selected fully contained Monte-Carlo $\nu_\mu$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Fully contained $\nu_\mu$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of selected fully contained Monte-Carlo $\nu_\mu$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Partially contained $\nu_e$CC data, signal, background, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_\mu$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining intrinsic $\nu_e$CC events. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Partially contained $\nu_\mu$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of selected partially contained Monte-Carlo $\nu_\mu$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Partially contained $\nu_\mu$CC true neutrino energy vs reconstructed neutrino energy. Each bin shows the relative number of selected partially contained Monte-Carlo $\nu_\mu$ CC events with true neutrino interaction vertex in the fiducial volume (3 cm inside the TPC active volume) which have the corresponding true neutrino energy and reconstructed neutrino energy values. Each axis has 60 bins covering an energy range from 0 to 3 GeV, corresponding to 0.05 GeV per bin.

Fully contained $\nu_\mu$CC data, signal, and background predictions. Events in the $\nu_e$CC or $\pi^0$ selections have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_e$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC events.

Fully contained $\nu_e$CC data, signal, background, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_\mu$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining intrinsic $\nu_e$CC events. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Fully contained $\nu_e$CC data, signal, background, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_\mu$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining intrinsic $\nu_e$CC events. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Partially contained $\nu_\mu$CC data, signal, and background predictions. Events in the $\nu_e$CC or $\pi^0$ selections have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_e$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC events.

Partially contained $\nu_e$CC data, signal, background, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_\mu$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining intrinsic $\nu_e$CC events. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Partially contained $\nu_e$CC data, signal, background, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_\mu$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining intrinsic $\nu_e$CC events. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Fully contained $\nu_\mu$CC$\pi^0$ data, signal, background predictions. Events in the $\nu_e$CC selection have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed $\pi^0$ kinetic energy greater than 1 GeV. The background includes neutral current events, $\nu_e$CC events, $\nu_\mu$CC events without a $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC$\pi^0$ events.

Fully contained $\nu_\mu$CC data, signal, and background predictions. Events in the $\nu_e$CC or $\pi^0$ selections have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_e$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC events.

Fully contained $\nu_\mu$CC data, signal, and background predictions. Events in the $\nu_e$CC or $\pi^0$ selections have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_e$CC events, $\nu_\mu$CC events with greater than or equal to one $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC events.

Partially contained $\nu_\mu$CC$\pi^0$ data, signal, and background predictions. Events in the $\nu_e$CC selection have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed $\pi^0$ kinetic energy greater than 1 GeV. The background includes neutral current events, $\nu_e$CC events, $\nu_\mu$CC events without a $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC$\pi^0$ events.

Partially contained $\nu_\mu$CC data, signal, and background predictions. Events in the $\nu_e$CC or $\pi^0$ selections have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_e$CC events, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC events.

Partially contained $\nu_\mu$CC data, signal, and background predictions. Events in the $\nu_e$CC or $\pi^0$ selections have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The background includes neutral current events, $\nu_e$CC events, $\nu_\mu$CC events with greater than or equal to one $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC events.

NC$\pi^0$ data, signal, and background predictions. Events in the $\nu_e$CC selection have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed $\pi^0$ kinetic energy greater than 1 GeV. The background includes $\nu_e$CC events, $\nu_\mu$CC events, neutral current events without a $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining NC$\pi^0$ events.

Fully contained $\nu_\mu$CC$\pi^0$ data, signal, background predictions. Events in the $\nu_e$CC selection have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed $\pi^0$ kinetic energy greater than 1 GeV. The background includes neutral current events, $\nu_e$CC events, $\nu_\mu$CC events without a $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC$\pi^0$ events.

Fully contained $\nu_\mu$CC$\pi^0$ data, signal, background predictions. Events in the $\nu_e$CC selection have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed $\pi^0$ kinetic energy greater than 1 GeV. The background includes neutral current events, $\nu_e$CC events, $\nu_\mu$CC events without a $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC$\pi^0$ events.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.0), the standard prediction with no low energy excess. No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

Partially contained $\nu_\mu$CC$\pi^0$ data, signal, and background predictions. Events in the $\nu_e$CC selection have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed $\pi^0$ kinetic energy greater than 1 GeV. The background includes neutral current events, $\nu_e$CC events, $\nu_\mu$CC events without a $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC$\pi^0$ events.

Partially contained $\nu_\mu$CC$\pi^0$ data, signal, and background predictions. Events in the $\nu_e$CC selection have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed $\pi^0$ kinetic energy greater than 1 GeV. The background includes neutral current events, $\nu_e$CC events, $\nu_\mu$CC events without a $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining $\nu_\mu$CC$\pi^0$ events.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.1). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

NC$\pi^0$ data, signal, and background predictions. Events in the $\nu_e$CC selection have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed $\pi^0$ kinetic energy greater than 1 GeV. The background includes $\nu_e$CC events, $\nu_\mu$CC events, neutral current events without a $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining NC$\pi^0$ events.

NC$\pi^0$ data, signal, and background predictions. Events in the $\nu_e$CC selection have been removed. Note that the rightmost bin is an overflow bin, containing all events with reconstructed $\pi^0$ kinetic energy greater than 1 GeV. The background includes $\nu_e$CC events, $\nu_\mu$CC events, neutral current events without a $\pi^0$, events with a true neutrino interaction vertex outside the fiducial volume (3 cm inside the TPC active volume), and cosmic ray backgrounds. The signal includes the remaining NC$\pi^0$ events.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.2). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.0), the standard prediction with no low energy excess. No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.0), the standard prediction with no low energy excess. No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.3). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.1). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.1). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.4). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.2). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.2). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.5). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.3). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.3). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.6). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.4). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.4). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.7). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.5). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.5). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.8). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.6). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.6). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.9). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.7). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.7). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.0), the median unfolded MiniBooNE LEE under a $\nu_e$CC hypothesis. No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.8). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.8). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.1). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.9). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.9). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.2). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.0), the median unfolded MiniBooNE LEE under a $\nu_e$CC hypothesis. No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.0), the median unfolded MiniBooNE LEE under a $\nu_e$CC hypothesis. No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.3). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.1). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.1). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.4). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.2). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.2). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.5). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.3). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.3). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.6). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.4). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.4). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.7). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.5). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.5). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.8). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.6). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.6). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.9). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.7). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.7). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=2.0). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.8). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.8). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.9). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.9). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=2.0). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

7 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=2.0). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC PC channel. The 53-78th bins/rows/columns correspond to the $\nu_\mu$CC FC channel. The 79-104th bins/rows/columns correspond to the $\nu_\mu$CC PC channel. The 105-115th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 116-126th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 127-137th bins/rows/columns correspond to the NC$\pi^0$ channel.

Fully contained $\nu_e$CC $0pX\pi$ data, prediction, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Partially contained $\nu_e$CC $0pX\pi$ data, prediction, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Fully contained $\nu_e$CC $NpX\pi$ data, prediction, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Partially contained $\nu_e$CC $NpX\pi$ data, prediction, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Fully contained $\nu_\mu$CC $0pX\pi$ data, prediction, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Partially contained $\nu_\mu$CC $0pX\pi$ data, prediction, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Fully contained $\nu_\mu$CC $NpX\pi$ data, prediction, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

Partially contained $\nu_\mu$CC $NpX\pi$ data, prediction, and LEE(x=1) predictions. Note that the rightmost bin is an overflow bin, containing all events with reconstructed neutrino energy greater than 2.5 GeV. The LEE(x=1) includes the predicted excess from an unfolding of the MiniBooNE LEE under a $\nu_e$CC hypothesis.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.0), the standard prediction with no low energy excess. No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.1). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.2). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.3). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.4). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.5). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.6). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.7). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.8). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=0.9). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.0), the median unfolded MiniBooNE LEE under a $\nu_e$CC hypothesis. No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.1). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.2). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.3). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.4). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.5). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.6). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.7). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.8). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=1.9). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

11 channel covariance matrix showing uncertainties and correlations between bins due to flux uncertainties, cross-section uncertainties, hadron reinteraction uncertainties, detector systematic uncertainties, Monte-Carlo statistical uncertainties, and dirt (outside cryostat) uncertainties. For the data statistical uncertainty covariance matrix, (only diagonal elements, not included here), the Neyman, Pearson, or combined Neyman and Pearson (CNP) techniques can be used. This corresponds to LEE(x=2.0). No constraints have been applied at this stage. The 1-26th bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ FC channel. The 27-52nd bins/rows/columns correspond to the $\nu_e$CC $0pX\pi$ PC channel. The 53-78th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ FC channel. The 79-104th bins/rows/columns correspond to the $\nu_e$CC $NpX\pi$ PC channel. The 105-130th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ FC channel. The 131-156th bins/rows/columns correspond to the $\nu_\mu$CC $0pX\pi$ PC channel. The 157-182nd bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ FC channel. The 183-208th bins/rows/columns correspond to the $\nu_\mu$CC $NpX\pi$ PC channel. The 209-219th bins/rows/columns correspond to the CC$\pi^0$ FC channel. The 220-230th bins/rows/columns correspond to the CC$\pi^0$ PC channel. The 231-241st bins/rows/columns correspond to the NC$\pi^0$ channel.

Data from Figure 11a

Model-independent per-channel efficiencies calculated in the fiducial volume of the High Mass channel for the $2e2\mu$ final state.

Model-independent per-channel efficiencies calculated in the fiducial volume of the High Mass channel for the $4e$ final state.

Model-dependent per-channel fiducial region acceptances for the $H\to aa\to 4\mu$ process. The regions of 2 GeV to 4.4 GeV, and 8 GeV to 12 GeV are each excluded by the quarkonia veto. Both, the Low Mass and the High Mass channel, include $m_X = $ 15 GeV at their signal region's edges, hence the dual entry for $m_X = $ 15 GeV.

Model-dependent per-channel fiducial region acceptances for the $H\to Z_dZ_d\to 4\ell$ process. The regions of 2 GeV to 4.4 GeV, and 8 GeV to 12 GeV are each excluded by the quarkonia veto. Both, the Low Mass and the High Mass channel, include $m_X = $ 15 GeV at their signal region's edges, hence the dual entry for $m_X = $ 15 GeV.

Model-dependent per-channel fiducial region acceptances for the $H\to Z_dZ_d\to 2e2\mu$ process.

Model-dependent per-channel fiducial region acceptances for the $H\to Z_dZ_d\to 4e$ process.

Upper limits at 95% CL on fiducial cross-sections for the $H\rightarrow XX \rightarrow 4\mu$ process. The step change at $m_X$ = 15 GeV is due to the change in efficiency caused by the change in fiducial phase-space definition. The regions of 2 GeV to 4.4 GeV, and 8 GeV to 12 GeV are each excluded by the quarkonia veto.

Upper limits at 95% CL on fiducial cross-sections for the $H\rightarrow XX \rightarrow 4e$ process.

Upper limits at 95% CL on fiducial cross-sections for the $H\rightarrow XX \rightarrow 2e2\mu$ process.

Observed and expected upper limits at 95% CL for the cross section of the $H\to Z_dZ_d\to 4\ell$ process, assuming SM Higgs boson production via the gluon-gluon fusion process. All final states are combined.

Observed and expected upper limits at 95% CL for the cross sections of the $H\to Z_dZ_d\to 4\mu$ processes, assuming SM Higgs boson production via the gluon-gluon fusion process. The regions of 2 GeV to 4.4 GeV, and 8 GeV to 12 GeV are each excluded by the quarkonia veto. The step changes at $m_{Z_d}= 15 \,\text{GeV}$ are due to the change in selection from the Low Mass to the High Mass analysis.

Observed and expected upper limits at 95% CL for the cross sections of the $H\to aa\to 4\mu$ processes, assuming SM Higgs boson production via the gluon-gluon fusion process. The regions of 2 GeV to 4.4 GeV, and 8 GeV to 12 GeV are each excluded by the quarkonia veto. The step changes at $m_{Z_d}= 15 \,\text{GeV}$ are due to the change in selection from the Low Mass to the High Mass analysis.

Model-independent efficiencies for the $H\to ZX$ process for the $4\mu$ and $2e2\mu$ final states combined calculated in the $ZX$ fiducial volume.

Model-independent efficiencies for the $H\to ZX$ process for the $4\mu$, $2e2\mu$, $2\mu 2e$, and $4e$ final states combined calculated in the $ZX$ fiducial volume.

Model-independent efficiencies for the $H\to ZX$ process for the $2\mu 2e$, and $4e$ final states combined calculated in the $ZX$ fiducial volume.

Model-dependent per-channel fiducial region acceptances for the $H\to ZZ_d\to 4\ell$ process for the $4\mu$, and $2e2\mu$ final states.

Model-dependent per-channel fiducial region acceptances for the $H\to ZZ_d\to 4\ell$ process for the $4\mu$, $2e2\mu$, $2\mu 2e$, and $4e$ final states.

Model-dependent per-channel fiducial region acceptances for the $H\to ZZ_d\to 4\ell$ process for the $2\mu 2e$, and $4e$ final states.

Per-channel upper limit at 95% CL on the fiducial cross-section for the $H\rightarrow ZX \to 2\ell2e$ process.

Per-channel upper limit at 95% CL on the fiducial cross-section for the $H\rightarrow ZX \to 2\ell2\mu$ process.

Observed and expected upper limits at 95% CL for the cross sections of the $H\to ZZ_d\to 4\ell$ process, assuming SM Higgs~boson production via the gluon-gluon fusion process.

Observed and expected upper limits at 95% CL for the cross sections of the $H\to Za\to 2\ell2\mu$ process, assuming SM Higgs~boson production via the gluon-gluon fusion process.

95% CL upper limits on the cross section times the model-dependent branching ratio divided by the SM Higgs boson production cross section for the $H\rightarrow Z_dZ_d$ process for the benchmark HAHM. The regions of 2 GeV to 4.4 GeV, and 8 GeV to 12 GeV are each excluded by the quarkonia veto. The step changes at $m_{Z_d}= 15 \,\text{GeV}$ are due to the change in selection from the Low Mass to the High Mass analysis.

95% CL upper limits on the cross section times the model-dependent branching ratio divided by the SM Higgs boson production cross section for the $H\rightarrow aa$ process for the benchmark 2HDM+S model. The regions of 2 GeV to 4.4 GeV, and 8 GeV to 12 GeV are each excluded by the quarkonia veto. The step changes at $m_{Z_d}= 15 \,\text{GeV}$ are due to the change in selection from the Low Mass to the High Mass analysis.

Upper limit at 95% CL on the effective Higgs mixing parameter $\kappa^\prime=\kappa \, m_H^2 / |m_H^2-m_S^2|$. The regions of 2 GeV to 4.4 GeV, and 8 GeV to 12 GeV are each excluded by the quarkonia veto. The step changes at $m_{Z_d}= 15 \,\text{GeV}$ are due to the change in selection from the Low Mass to the High Mass analysis.

Upper limit at 95% CL on the $Z_d$ kinetic mixing parameter $\epsilon$, assuming the SM Higgs boson production cross section.

Upper limit at 95% CL on the $Z$-$Z_d$ mass mixing parameter $\delta^2 \times \textrm{BR}(Z_d\to \ell\ell)$, assuming the SM Higgs boson production cross section.

The background estimate and the observed data in the number of tagged jets >= 2 bin, for each of the seven validation samples (VS$_{1}$ through VS$_{7}$), along with the signal sample (Sig S). Signal model distributions for scalar masses of 15 and 55 GeV with a proper mean decay length of 20 mm are also shown. The Higgs boson branching fraction to long-lived scalars (B(H$\rightarrow $ SS)) is set to 20%.

Exclusion limits at 95% CL on the Higgs boson branching fraction to long-lived scalars B(H$\rightarrow$ SS) as a function of the mean proper decay length of the scalar for scalar mass 15 GeV and the S$\rightarrow d\bar{d}$ decay mode.

Exclusion limits at 95% CL on the Higgs boson branching fraction to long-lived scalars B(H$\rightarrow$ SS) as a function of the mean proper decay length of the scalar for scalar mass 40 GeV and the S$\rightarrow d\bar{d}$ decay mode.

Exclusion limits at 95% CL on the Higgs boson branching fraction to long-lived scalars B(H$\rightarrow$ SS) as a function of the mean proper decay length of the scalar for scalar mass 55 GeV and the S$\rightarrow d\bar{d}$ decay mode.

Exclusion limits at 95% CL on the Higgs boson branching fraction to long-lived scalars B(H$\rightarrow$ SS) as a function of the mean proper decay length of the scalar for scalar mass 15 GeV and the S$\rightarrow b\bar{b}$ decay mode.

Exclusion limits at 95% CL on the Higgs boson branching fraction to long-lived scalars B(H$\rightarrow$ SS) as a function of the mean proper decay length of the scalar for scalar mass 40 GeV and the S$\rightarrow b\bar{b}$ decay mode.

Exclusion limits at 95% CL on the Higgs boson branching fraction to long-lived scalars B(H$\rightarrow$ SS) as a function of the mean proper decay length of the scalar for scalar mass 55 GeV and the S$\rightarrow b\bar{b}$ decay mode.

Distribution of the three leading leptons flavour in the SR-WZ with uncertainties evaluated after the inclusive cross section fit

Efficiency, acceptance, and proportion of events with leptonic tau decays in WZ production

WZ fiducial cross section in the four flavour exclusive and the flavour inclusive channels

WZ total cross section extrapolated from the four flavour exclusive and the flavour inclusive channels

Distribution of the total lepton charge in the SR-WZ with uncertainties evaluated after the inclusive cross section fit

W$^{+}$Z fiducial cross section in the four flavour exclusive and the flavour inclusive channels

W$^{-}$Z fiducial cross section in the four flavour exclusive and the flavour inclusive channels

WZ charge asymmetry ratio measured on each of the four flavour exclusive and the flavour inclusive channels

Distribution of the cosine of the W polarization angle times total lepton charge in the SR-WZ with uncertainties evaluated after the W polarization fit

Distribution of the cosine of the Z polarization angle in the SR-WZ with uncertainties evaluated after the Z polarization fit

Best fits to the W and Z polarization fractions

2D confidence regions at the 68, 95, and 99% CL in the $f_O^W$-$f_{L}^W-f_R^W$ plane

2D confidence regions at the 68, 95, and 99% CL in the $f_O^Z$-$f_{L}^Z-f_R^Z$ plane

Distribution of the invariant mass of the WZ system in the SR-WZ with uncertainties evaluated after the inclusive cross section fit

Best fit values and one dimensional confidence regions in several EFT coefficients obtained from the EFT fit considering both the SM interferences and purely BSM (order $\Lambda^{-2}$ and $\Lambda^{-4}$) terms

2D confidence regions at the 68, 95, and 99% CL in the $c_{www}$-$c_{w}$ plane

2D confidence regions at the 68, 95, and 99% CL in the $c_{w}$-$c_{b}$ plane

2D confidence regions at the 68, 95, and 99% CL in the $c_{www}$-$c_{w}$ plane

Best fit values and one dimensional confidence regions in several EFT coefficients obtained from the EFT fit considering only the SM-EFT interference (order $\Lambda^{-2}$) terms

Evolution of the best fit and expected and observed 95% CI for the $c_{w}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $c_{b}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $c_{www}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $\tilde{c}_{www}$ parameter as a function of the cutoff scale

Evolution of the best fit and expected and observed 95% CI for the $\tilde{c}_{w}$ parameter as a function of the cutoff scale

Differential cross section with respect to the transverse momentum of the Z boson

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the Z boson

Response matrix for the $p_{T}$ of the Z boson obtained with POWHEG

Differential cross section with respect to the transverse momentum of the leading jet

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the leading jet

Response matrix for the $p_{T}$ of the leading jet obtained with POWHEG

Differential cross section with respect to the jet multiplicity

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the jet multiplicity

Response matrix for the jet multiplicity obtained with POWHEG

Differential cross section with respect to the invariant mass of the WZ system

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the invariant mass of the WZ system

Response matrix for the invariant mass of the WZ system obtained with POWHEG

Differential cross section with respect to the transverse momentum of the lepton associated to the W boson

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson

Response matrix for the $p_{T}$ of the lepton associated to the W boson obtained with POWHEG

Differential cross section with respect to the transverse momentum of the lepton associated to the W boson, W$^{+}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson, W$^{+}$Z only

Differential cross section with respect to the transverse momentum of the lepton associated to the W boson, W$^{-}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the $p_{T}$ of the lepton associated to the W boson, W$^{-}$Z only

Differential cross section with respect to the cosine of the W polarization angle times total lepton charge

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge

Response matrix for the cosine of the W polarization angle times total lepton charge obtained with POWHEG

Differential cross section with respect to the cosine of the W polarization angle times total lepton charge, W$^{+}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge, W$^{+}$Z only

Differential cross section with respect to the cosine of the W polarization angle times total lepton charge, W$^{-}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the W polarization angle times total lepton charge, W$^{-}$Z only

Differential cross section with respect to the cosine of the Z polarization angle

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle

Response matrix for the cosine of the Z polarization angle obtained with POWHEG

Differential cross section with respect to the cosine of the Z polarization angle, W$^{+}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle, W$^{+}$Z only

Differential cross section with respect to the cosine of the Z polarization angle, W$^{-}$Z only

Correlation matrix for the unfolded results obtained using NNLO bias, area con-straint, and no additional regularization for the cosine of the Z polarization angle, W$^{-}$Z only

Parton dijet $M_{inv}$ vs $A_{LL}$ values with associated uncertainties, for topology C.

Parton dijet $M_{inv}$ vs $A_{LL}$ values with associated uncertainties, for topology D.

The correlation matrix for the point-to-point uncertainties (statistical and systematics) for the inclusive jet measurements.The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (statistical and systematics) for the inclusive jet measurements coupling with the forward-forward dijet measurements (topology A). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (statistical and systematics) for the inclusive jet measurements coupling with the forward-forward dijet measurements (topology B). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (statistical and systematics) for the inclusive jet measurements coupling with the forward-forward dijet measurements (topology C). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (statistical and systematics) for the inclusive jet measurements coupling with the forward-forward dijet measurements (topology D). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) for forward-forward dijet measurements (topology A). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) coupling forward-forward dijet measurements (topology A) with forward-central dijet measurements (topology B). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) coupling forward-forward dijet measurements (topology A) with forward-central dijet measurements (topology C). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) coupling forward-forward dijet measurements (topology A) with forward-central dijet measurements (topology D). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) for forward-forward dijet measurements (topology B). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) coupling forward-forward dijet measurements (topology B) with forward-central dijet measurements (topology C). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) coupling forward-forward dijet measurements (topology B) with forward-central dijet measurements (topology D). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) for forward-forward dijet measurements (topology C). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) coupling forward-forward dijet measurements (topology C) with forward-central dijet measurements (topology D). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.

The correlation matrix for the point-to-point uncertainties (systematics only) for forward-forward dijet measurements (topology D). The relative luminosity and beam polarization uncertainties are not included because they are the same for all points.