Ratio of differential cross section of AK1 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK2 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK2 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK2 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK2 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK3 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK3 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK3 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK3 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK4 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK4 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK4 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK4 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK5 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK5 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK5 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK5 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK6 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK6 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK6 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK6 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK7 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK7 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK7 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK7 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK8 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK8 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK8 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK8 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK9 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK9 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK9 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK9 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK10 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK10 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK10 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK10 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK11 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK11 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK11 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK11 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK12 jets with respect to AK4 jets a function of jet PT in the rapidity range |y|<0.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK12 jets with respect to AK4 jets a function of jet PT in the rapidity range 0.5<|y|<1.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK12 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.0<|y|<1.5. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of differential cross section of AK12 jets with respect to AK4 jets a function of jet PT in the rapidity range 1.5<|y|<2.0. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 84 < p_{T} < 97 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 97 < p_{T} < 133 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 133 < p_{T} < 196 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 196 < p_{T} < 272 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 272 < p_{T} < 330 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 330 < p_{T} < 395 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 395 < p_{T} < 468 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 468 < p_{T} < 548 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 548 < p_{T} < 638 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range |y|<0.5 and PT 638 < p_{T} < 1588 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 84 < p_{T} < 97 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 97 < p_{T} < 133 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 133 < p_{T} < 196 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 196 < p_{T} < 272 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 272 < p_{T} < 330 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 330 < p_{T} < 395 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 395 < p_{T} < 468 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 468 < p_{T} < 548 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 548 < p_{T} < 638 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 0.5<|y|<1.0 and PT 638 < p_{T} < 1588 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 84 < p_{T} < 97 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 97 < p_{T} < 133 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 133 < p_{T} < 196 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 196 < p_{T} < 272 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 272 < p_{T} < 330 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 330 < p_{T} < 395 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 395 < p_{T} < 468 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 468 < p_{T} < 548 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 548 < p_{T} < 638 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.0<|y|<1.5 and PT 638 < p_{T} < 1588 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 84 < p_{T} < 97 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 97 < p_{T} < 133 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 133 < p_{T} < 196 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 196 < p_{T} < 272 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 272 < p_{T} < 330 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 330 < p_{T} < 395 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 395 < p_{T} < 468 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 468 < p_{T} < 548 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 548 < p_{T} < 638 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

Ratio of cross section of inclusive anti-KT jets with respect to AK4 jets as a function of jet size in the rapidity range 1.5<|y|<2.0 and PT 638 < p_{T} < 1588 GeV. The nonperturbative correction can be used to scale fixed-order theory prediction to compare to data at particle level.

$\Upsilon$(1S) differential cross section as a function of $p_{\rm T}$, at forward rapidity at $\sqrt{s_{\rm NN}}$ = 8.16 TeV .The first uncertainty is statistical, while the second is the systematic.

$\Upsilon$(1S) differential cross section as a function of $p_{\rm T}$, at backward rapidity at $\sqrt{s_{\rm NN}}$ = 8.16 TeV .The first uncertainty is statistical, while the second is the systematic.

Ratio of $\Upsilon$(2S) over $\Upsilon$(1S) yields in p--Pb collisions at $\sqrt{s_{\rm NN}}$ = 8.16 TeV .The first uncertainty is statistical, while the second is the systematic.

Ratio of $\Upsilon$(3S) over $\Upsilon$(1S) yields in p--Pb collisions at $\sqrt{s_{\rm NN}}$ = 8.16 TeV .The first uncertainty is statistical, while the second is the systematic.

$\Upsilon$(1S) $R_{\rm pPb}$ values at $\sqrt{s_{\rm NN}}$ = 8.16 TeV. The first uncertainty is statistical, the second is the uncorrelated systematic, while the third one is a correlated systematic uncertainty.

$\Upsilon$(1S) $R_{\rm pPb}$ as a function of $y_{\rm {cms}}$ at $\sqrt{s_{\rm NN}}$ = 8.16 TeV. The first uncertainty is statistical, the second is the uncorrelated systematic, while the third one is a correlated systematic uncertainty.

$\Upsilon$(1S) $R_{\rm pPb}$ as a function of $p_{\rm T}$ at backward rapidity at $\sqrt{s_{\rm NN}}$ = 8.16 TeV. The first uncertainty is statistical, the second is the uncorrelated systematic, while the third one is a correlated systematic uncertainty.

$\Upsilon$(1S) $R_{\rm pPb}$ as a function of $p_{\rm T}$ at forward rapidity at $\sqrt{s_{\rm NN}}$ = 8.16 TeV. The first uncertainty is statistical, the second is the uncorrelated systematic, while the third one is a correlated systematic uncertainty.

$\Upsilon$(1S) $Q_{\rm pPb}$ as a function of $<N_{\rm coll}>$ at backward rapidity at $\sqrt{s_{\rm NN}}$ = 8.16 TeV. The first uncertainty is statistical, the second is the uncorrelated systematic, while the third one is a correlated systematic uncertainty.

$\Upsilon$(1S) $Q_{\rm pPb}$ as a function of $<N_{\rm coll}>$ at forward rapidity at $\sqrt{s_{\rm NN}}$ = 8.16 TeV. The first uncertainty is statistical, the second is the uncorrelated systematic, while the third one is a correlated systematic uncertainty.

$\Upsilon$(1S) $R_{\rm pPb}$ values at $\sqrt{s_{\rm NN}}$ = 8.16 TeV. The first uncertainty is statistical, the second is the uncorrelated systematic, while the third one is a correlated systematic uncertainty.

$\Upsilon$(2S) $R_{\rm pPb}$ values at $\sqrt{s_{\rm NN}}$ = 8.16 TeV. The first uncertainty is statistical, the second is the uncorrelated systematic, while the third one is a correlated systematic uncertainty.

$\Upsilon$(3S) $R_{\rm pPb}$ values at $\sqrt{s_{\rm NN}}$ = 8.16 TeV. The first uncertainty is statistical, the second is the uncorrelated systematic, while the third one is a correlated systematic uncertainty.

Ratio of $\Upsilon$(2S) to $\Upsilon$(1S) $R_{\rm pPb}$ at $\sqrt{s_{\rm NN}}$ = 8.16 TeV as a function of $y_{\rm cms}$ .The first uncertainty is statistical, while the second is the systematic.

Ratio of $\Upsilon$(3S) to $\Upsilon$(1S) $R_{\rm pPb}$ at $\sqrt{s_{\rm NN}}$ = 8.16 TeV as a function of $y_{\rm cms}$ .The first uncertainty is statistical, while the second is the systematic.

Nuclear modification factors $R_{AA}$ as a function of $E_{T}^{\gamma}$ measured in the 0–100% centrality ranges in PbPb.

The minus $1\sigma$ variation of observed exclusion contour obtained by varying the signal cross section within its uncertainty. The contour is given as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$.

The plus $1\sigma$ variation of observed exclusion contour obtained by varying the signal cross section within its uncertainty. The contour is given as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$.

The minus $1\sigma$ variation of expected exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties (excluding signal cross section uncertainties). The contour is given as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$.

The plus $1\sigma$ variation of expected exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties (excluding signal cross section uncertainties). The contour is given as a function of the $\it{m}_{\tilde{\chi}^{0}_{1}}$ vs. $\it{m}_{\tilde{t}}$.

The observed exclusion contour at 95% CL as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$. Points that are within the contours are excluded.

The expected exclusion contour at 95% CL as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$. Points that are within the contours are excluded.

The minus $1\sigma$ variation of observed exclusion contour obtained by varying the signal cross section within its uncertainty. The contour is given as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$

The plus $1\sigma$ variation of observed exclusion contour obtained by varying the signal cross section within its uncertainty. The contour is given as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$

The plus $1\sigma$ variation of expected exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties (excluding signal cross section uncertainties). The contour is given as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$

The minus $1\sigma$ variation of expected exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties (excluding signal cross section uncertainties). The contour is given as a function of the $\it{m}_{LQ_{3}^{u}}$ vs. $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau)$

Model dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{0}_{1})$ signal grid. The column titled 'Leading Region' stores information on which of the fit regions (SRA-B, SRC or SRD) is the dominant based on the expected CLs values.

Expected model dependent upper limit on the cross section for the $(\tilde{t},\tilde{\chi}^{0}_{1})$ signal grid. The column titled 'Leading Region' stores information on which of the fit regions (SRA-B, SRC or SRD) is the dominant based on the expected CLs values.

Model dependent upper limit on the cross section for the $LQ_{3}^{u}$ signal grid with $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau))=0$ %. Only the SRA-B fit region is considered in this interpretation.

Expected model dependent upper limit on the cross section for the $LQ_{3}^{u}$ signal grid with $\mathrm{BR}(\it{m}_{LQ_{3}^{u}}\rightarrow b \tau))=0$ %. Only the SRA-B fit region is considered in this interpretation.

The distributions of $S$ in SRA-TW. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of $\it{m}^{\mathrm{R=1.2}}_{1}$ in SRB-TT. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of R$_{ISR}$ in SRC signal regions before R$_{ISR}$ cuts are applied. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of $E^{miss}_{T}/\sqrt{H_{T}}$ in SRD0. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of $E^{miss}_{T}/\sqrt{H_{T}}$ in SRD1. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

The distributions of $E^{miss}_{T}/\sqrt{H_{T}}$ in SRD2. For each bin yields for the data, total SM prediction and a representative signal point are provided. The SM prediction is provided with the MC statistical uncertainties, labeled 'stat', and the remaining uncertainties, labeled 'syst' that include detector-related systematic uncertainties and theoretical uncertainties. The signal predictions is provided with the MC statistical uncertainties only. The rightmost bin includes overflow events.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (1300,1)\ \mathrm{GeV} $ in SRA-TT. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 30000 raw MC events were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (1300,1)\ \mathrm{GeV} $ in SRA-TW. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 30000 raw MC events were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (1300,1)\ \mathrm{GeV} $ in SRA-T0. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 30000 raw MC events were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (700,400)\ \mathrm{GeV} $ in signal regions SRB-TT, SRB-TW and SRB-T0. The regions differ by the last cut applied. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 60000 raw MC events were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (500,327)\ \mathrm{GeV} $ in regions SRC-1, SRC-2, SRC-3, SRC-4 and SRC-5. The regions differ by the last cut applied. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 150000 raw MC events with filter efficiency of 0.384 were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (550,500)\ \mathrm{GeV} $ in SRD0. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 90000 raw MC events with filter efficiency of 0.428 were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (550,500)\ \mathrm{GeV} $ in SRD1. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 90000 raw MC events with filter efficiency of 0.428 were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Cutflow for the reference point $(\it{m}_{\tilde{t}}, \it{m}_{\tilde{\chi}^{0}_{1}})= (550,500)\ \mathrm{GeV} $ in SRD2. The column labelled ''Weighted yield'' shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$. A notable exception concerns results in the first row, labelled ''Total'', that corresponds to plain $\sigma \cdot \mathcal{L}$ expected. The ''Derivation skim'' includes the requirements that $H_{T}$, the scalar sum of $p_{T}$ of jets and leptons, $H_{T}>150\ \mathrm{GeV}$ or that a ''baseline'' electron or muon has $p_{T}>20\ \mathrm{GeV}$. The definition of ''baseline'' electron/muons, lepton and $\tau$ vetos are described in the main body of the paper. In total 90000 raw MC events with filter efficiency of 0.428 were generated prior to the specified cuts, with the column ''Unweighted yield'' collecting the numbers after each cut.

Signal acceptance in SRA-TT for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRA-TT for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRA-TW for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRA-TW for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRA-T0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRA-T0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRB-TT for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRB-TT for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRB-TW for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRB-TW for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRB-T0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$

Signal efficiency in SRB-T0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in %.

Signal acceptance in SRC1 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRC1 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRC2 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRC2 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRC3 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRC3 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRC4 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRC4 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ plane showed in the paper plot.

Signal acceptance in SRC5 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ plane showed in the paper plot.

Signal efficiency in SRC5 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRD0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRD0 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRD1 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRD1 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal acceptance in SRD2 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the acceptance given in the table is multiplied by factor of $10^{5}$ and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Signal efficiency in SRD2 for simplified $(\tilde{t},\tilde{\chi^{0}_1})$ model. Please mind that the efficiency in the table is reported in % and the results are given here in the $\it{m}_{\tilde{t}}-\it{m}_{\tilde{\chi}^{0}_{1}}$ plane as opposed to the $\it{m}_{\tilde{t}}-\Delta(\it{m}_{\tilde{\chi}^{0}_{1}},\it{m}_{\tilde{t}})$ one showed in the paper plot.

Elliptic flow ($v_{2}$) of (anti-)$^{3}$He measured in Pb-Pb collisions at \sqrt{s_{\mathrm{NN}}} = 5.02 TeV for the centrality classes 0--20$\%$, 20--40$\%$, and 40--60$\%$. The statistical uncertainties are shown as vertical bars, systematic uncertainties as boxes.

Observed and expected 95% credibility-level upper limits on the cross-section times branching ratio for the simplified dark-matter model. The table also shows the corresponding $1\sigma$ and $2\sigma$ bands for the expected limits. The limits are calculated using jets in events with at least one isolated lepton ($e$ or $\mu$) with $p_\text{T}^\ell \ge 60$ GeV.

The 95% CL upper limits on the cross-section times acceptance times efficiency times branching ratio, as a function of the resonance mass, $m_X$. The limits are calculated using jets in events with at least one isolated lepton ($e$ or $\mu$) with $p_\text{T}^\ell \ge 60$ GeV. The limits are shown for a generic Gaussian signal with the width $\sigma_{X}/m_{X} =0$. The table also shows the expected limits and the corresponding $1\sigma$ and $2\sigma$ bands. The tabulated data has been used in Figure 04 of the publication.

The 95% CL upper limits on the cross-section times acceptance times efficiency times branching ratio, as a function of the resonance mass, $m_X$. The limits are calculated using jets in events with at least one isolated lepton ($e$ or $\mu$) with $p_\text{T}^\ell \ge 60$ GeV. The limits are shown for a generic Gaussian signal with the width $\sigma_{X}/m_{X} =0.05$. The table also shows the expected limits and the corresponding $1\sigma$ and $2\sigma$ bands. The tabulated data has been used in Figure 04 of the publication.

The 95% CL upper limits on the cross-section times acceptance times efficiency times branching ratio, as a function of the resonance mass, $m_X$. The limits are calculated using jets in events with at least one isolated lepton ($e$ or $\mu$) with $p_\text{T}^\ell \ge 60$ GeV. The limits are shown for a generic Gaussian signal with the width $\sigma_{X}/m_{X} =0.10$. The table also shows the expected limits and the corresponding $1\sigma$ and $2\sigma$ bands. The tabulated data has been used in Figure 04 of the publication.

The 95% CL upper limits on the cross-section times acceptance times efficiency times branching ratio, as a function of the resonance mass, $m_X$. The limits are calculated using jets in events with at least one isolated lepton ($e$ or $\mu$) with $p_\text{T}^\ell \ge 60$ GeV. The limits are shown for a generic Gaussian signal with the width $\sigma_{X}/m_{X} =0.15$. The table also shows the expected limits and the corresponding $1\sigma$ and $2\sigma$ bands. The tabulated data has been used in Figure 04 of the publication.

Truth-level cross-sections ($\sigma$), acceptances ($A$), detector efficiencies ($\epsilon$) and the detector-level visible cross-sections ($\sigma^{vis}$) for different masses of the $\rho_T$ model. Only statistical uncertainties on the acceptances and efficiencies are shown. The acceptance is defined by requiring jets with $m_{jj} >216$ GeV and $|\eta|<2.4$, as well as isolated leptons ($e$ or $\mu$) with $p_\text{T}^\ell \ge 60$ GeV and $|\eta^\mu|<2.5$ ($|\eta^e|<2.47$, excluding of the gap region 1.37--1.52) for muons (electrons). The tabulated data has been used in Figure 05a of the publication.

Truth-level cross-sections ($\sigma$), acceptances ($A$), detector efficiencies ($\epsilon$) and the detector-level visible cross-sections ($\sigma^{vis}$) for different masses of the $W'/Z'$ SSM model. Only statistical uncertainties on the acceptances and efficiencies are shown. The acceptance is defined by requiring jets with $m_{jj} >216$ GeV and $|\eta|<2.4$, as well as leptons with $p_\text{T}^\ell \ge 60$ GeV and $|\eta^\mu|<2.5$ ($|\eta^e|<2.47$ with exclusion of the gap region 1.37-1.52) for muons (electrons). The tabulated data has been used in Figure 05b of the publication.

Truth-level cross-sections ($\sigma$), acceptances ($A$), detector efficiencies ($\epsilon$) and the detector-level visible cross-sections ($\sigma^{vis}$) for different masses of the $H^+$ model for $\tan \beta=1$. Only statistical uncertainties on the acceptances and efficiencies are shown. The acceptance correction was calculated assuming $\tan \beta=1$ and the narrow-width approximation. The acceptance is defined by requiring jets with $m_{jj} >216$ GeV and $|\eta|<2.4$, as well as leptons with $p_\text{T}^\ell \ge 60$ GeV and $|\eta^\mu|<2.5$ ($|\eta^e|<2.47$ with exclusion of the gap region 1.37-1.52) for muons (electrons). The tabulated data has been used in Figure 05c of the publication.

Truth-level cross-sections ($\sigma$), acceptances ($A$), detector efficiencies ($\epsilon$) and the detector-level visible cross-sections ($\sigma^{vis}$) for different masses of the $Z'$ (DM) model. Only statistical uncertainties on the acceptances and efficiencies are shown. The acceptance is defined by requiring jets with $m_{jj} > 216$ GeV and $|\eta|<2.4$, as well as leptons with $p_\text{T}^\ell \ge 60$ GeV and $|\eta^\mu|<2.5$ ($|\eta^e|<2.47$ with exclusion of the gap region 1.37-1.52) for muons (electrons). The tabulated data has been used in Figure 05d of the publication.

Observed and expected limits at 95% CL on the cross-sections of narrow resonance $X$ to $HH$ as a function of the mass $m_{X}$.

Observed and expected limits at 95% CL on the cross-sections of broad resonance $X$ to $HH$ as a function of the mass $m_{X}$.

Observed and expected limits at 95% CL on the cross-sections of non-resonant $HH$ production via VBF as a function of the di-vector-boson-di-Higgs-boson coupling modifier $\kappa_{2V}$. The theory prediction of the cross-section as a function of $\kappa_{2V}$ is also shown. The minimum theory cross-section is 1.37 fb at $\kappa_{2V}=1.17$.

Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the low-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the low-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the low-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the low-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the low-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the low-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the low-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the low-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the low-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of fake tracklet in electroweak channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of muon background in electroweak channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in electroweak channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of signal ($\tau_{\tilde{\chi}_{1}^{\pm}}$ = 0.2 ns and $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV) in electroweak channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of total background in electroweak channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of observed data in electroweak channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of fake tracklet in strong channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of muon background in strong channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of hadron and electron background in strong channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of signal ($m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}} = 500 GeV$) in strong channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of total background in strong channel in the high-Emiss region.

Pixel-tracklet $p_{T}$ spectrum of observed data in strong channel in the high-Emiss region.

Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).

Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).

Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.

Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 0.2 ns.

Expected exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.

Observed exclusion limit at 95% CL obtained in the strong production channel in terms of the gluino and chargino masses. The limit is shown assuming a chargino lifetime 1.0 ns.

Expected exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).

Observed exclusion limit at 95% CL obtained in the electroweak production channel in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).

Model dependent upper limits on cross-section (fb) for the electroweak production are shown by grey numbers in terms of the chargino lifetime ($\tau_{\tilde{\chi}_{1}^{\pm}}$) and mass ($m_{\tilde{\chi}_{1}^{\pm}}$).

Total acceptance $\times$ efficiency of the electroweak channel. The total signal acceptance $\times$ efficiency is defined as the probability of an event passing the signal region selection when an electroweak gaugino pair is produced in a pp collision.

Total acceptance $\times$ efficiency of the strong channel. In white regions, no simulation sample is available. The left-upper triangle region is not allowed kinematically in wino-LSP scenarios. The total signal acceptance $\times$ efficiency is calculated relative to events in which the gluinos decay into electroweak gaugino pairs.

The generator-level acceptance after reconstruction, for selecting and reconstructing charginos as a function of the chargino $eta$ and chargino decay radius (at generator level).

The generator-level acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.

The generator-level acceptance after reconstruction, for selecting and reconstructing charginos as a function of the chargino $\eta$ and chargino decay radius (at generator level).

The generator-level acceptance $\times$ efficiency after reconstruction, for selecting and reconstructing charginos as a function of the chargino $\eta$ and chargino decay radius (at generator level). The acceptance $\times$ efficiency after reconstruction is the probability of a signal event, which passes all the event-level requirements, passing all the track/tracklet requirements after reconstruction.

Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in direct electroweak production with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.

Summary of the selection criteria, and the corresponding observed number of events in data as well as the expected number of signal events in simulation for two benchmark models: a chargino produced in the strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. The expected number of signal events is normalised to 36.1 fb${}^{-1}$. The signal selection efficiencies are also shown in parentheses. The first row shows the number of events after the application of detector and data quality conditions. Requirements below the dashed line are applied to tracks and tracklets.

The event and tracklet generator-level acceptance and selection efficiency for a few electroweak signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.

The event and tracklet generator-level acceptance and selection efficiency for a few strong signal models studied in this search. The last column shows the probability ($P$) for a reconstructed tracklet to have $p_{T}$ greater than 100 GeV.

Systematic uncertainties in the signal event yields at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. The uncertainty in the cross-section of the strong production is large due to the large effect from the PDF uncertainty.

Observed events, expected background for null signal, and expected signal yields for two benchmark models: electroweak channel with ($m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (400 GeV, 0.2 ns) and strong channel with ($m_{\tilde{g}}$, $m_{\tilde{\chi}_{1}^{\pm}}$, $\tau_{\tilde{\chi}_{1}^{\pm}}$) = (1600 GeV, 500 GeV, 0.2 ns) in the high-Emiss region. Also shown are the probability of a background-only experiment being more signal-like than observed ($p_0$) and the upper limit on the model-independent visible cross-section at 95\% CL. The uncertainty in the total background yield is different from the sum of uncertainties in quadrature due to anticorrelation between different backgrounds.

Effects of systematic uncertainties on the signal exclusion significance at $m_{\tilde{\chi}_{1}^{\pm}}$ = 400 GeV for the electroweak channel and at $m_{\tilde{g}}$ = 1600 GeV, $m_{\tilde{\chi}_{1}^{\pm}}$ = 500 GeV for the strong channel. The lifetime of the chargino is not relevant here. Effects of uncertainties on the fake-tracklet background is smaller in the strong channel analysis because the estimated number of the fake-tracket background events is small.

Cross-section upper limits for the strong production, presented in unit of fb. Left-upper triangle region is unphysical because the wino mass is larger than the gluino mass.

Lower limits at 95$\%$ CL on $\Lambda$ for the dimuon channel for different signal chiralities in the (constructive/destructive interference) SRs of the analysis.

Lower limits at 95$\%$ CL on $\Lambda$ for the dilepton combination for different signal chiralities in the (constructive/destructive interference) SRs of the analysis.

The dielectron data events passing the full selection.

The dimuon data events passing the full selection.

Measured second cumulants of anti-protons.

Measured first cumulants of protons.

Measured first cumulants of anti-protons.

Measured R1 values.

Measured R2 values.

R2 values from Model.

R1 values vs. delaY.

Global baryon number conservation.

Local baryon number conservation, deltaY_corr = 5.

Local baryon number conservation, deltaY_corr = 2.

HIJING results.

The relative statistical and systematic uncertainties in % from various sources for the absolute cross section measurements in bins of Z pt on dimuons.

The relative statistical and systematic uncertainties in % from various sources for the absolute cross section measurements in bins of Z pt on dielectrons.

The relative statistical and systematic uncertainties in % from various sources for the absolute cross section measurements in bins of |y(Z)| on dimuons.

The relative statistical and systematic uncertainties in % from various sources for the absolute cross section measurements in bins of |y(Z)| on dielectrons.

The relative statistical and systematic uncertainties in % from various sources for the absolute cross section measurements in bins of $\phi^{\scriptscriptstyle *}_\eta$ on dimuons.

The relative statistical and systematic uncertainties in % from various sources for the absolute cross section measurements in bins of $\phi^{\scriptscriptstyle *}_\eta$ on dielectrons.

The relative statistical and systematic uncertainties in % from various sources for the normalized cross section measurements in bins of Z pt on dimuons.

The relative statistical and systematic uncertainties in % from various sources for the normalized cross section measurements in bins of Z pt on dielectrons.

The relative statistical and systematic uncertainties in % from various sources for the normalized cross section measurements in bins of |y(Z)| on dimuons.

The relative statistical and systematic uncertainties in % from various sources for the normalized cross section measurements in bins of |y(Z)| on dielectrons.

The relative statistical and systematic uncertainties in % from various sources for the normalized cross section measurements in bins of $\phi^{\scriptscriptstyle *}_\eta$ on dimuons.

The relative statistical and systematic uncertainties in % from various sources for the normalized cross section measurements in bins of $\phi^{\scriptscriptstyle *}_\eta$ on dielectrons.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dimuon final state.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dimuon final state.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dimuon final state.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dimuon final state.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dimuon final state.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dielectron final state.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dielectron final state.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dielectron final state.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dielectron final state.

The relative statistical and systematic uncertainties in % from various sources for the absolute double-differential cross section measurements in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dielectron final state.

The measured absolute cross sections in bins of |y(Z)|, using dressed level leptons. The cross sections are normalized by the bin width.

The measured absolute cross sections in bins of Z pt, using dressed level leptons. The cross sections are normalized by the bin width.

The measured absolute cross sections in bins of $\phi^{\scriptscriptstyle *}_\eta$, using dressed level leptons. The cross sections are normalized by the bin width.

The measured absolute cross sections in bins of Z pt different |y(Z)| bins, using dressed level leptons. The cross sections are normalized by the bin width.

The measured normalized cross sections in bins of Z pt, using dressed level leptons.

The measured normalized cross sections in bins of $\phi^{\scriptscriptstyle *}_\eta$, using dressed level leptons.

The measured normalized cross sections in bins of |y(Z)|, using dressed level leptons.

The measured normalized cross sections (left) in bins of Z pt in |y(Z)| bins, using dressed level leptons.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt in the dimuon final state.

Covariance matrix using dressed level leptons for all bins used in bins of |y(Z)| in the dimuon final state.

Covariance matrix using dressed level leptons for all bins used in bins of $\phi^{\scriptscriptstyle *}_\eta$ in the dimuon final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dimuon final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dimuon final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dimuon final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dimuon final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dimuon final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt in the dielectron final state.

Covariance matrix using dressed level leptons for all bins used in bins of |y(Z)| in the dielectron final state.

Covariance matrix using dressed level leptons for all bins used in bins of $\phi^{\scriptscriptstyle *}_\eta$ in the dielectron final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dielectron final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dielectron final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dielectron final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dielectron final state.

Covariance matrix using dressed level leptons for all bins used in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dielectron final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt in the dimuon final state.

Covariance matrix using born level leptons for all bins used in bins of |y(Z)| in the dimuon final state.

Covariance matrix using born level leptons for all bins used in bins of $\phi^{\scriptscriptstyle *}_\eta$ in the dimuon final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dimuon final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dimuon final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dimuon final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dimuon final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dimuon final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt in the dielectron final state.

Covariance matrix using born level leptons for all bins used in bins of |y(Z)| in the dielectron final state.

Covariance matrix using born level leptons for all bins used in bins of $\phi^{\scriptscriptstyle *}_\eta$ in the dielectron final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dielectron final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dielectron final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dielectron final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dielectron final state.

Covariance matrix using born level leptons for all bins used in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dielectron final state.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt in the dimuon final state.

The measured absolute cross sections in bins of |y(Z)|, using born level leptons. The cross sections are normalized by the bin width.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of |y(Z)| in the dimuon final state.

The measured absolute cross sections in bins of Z pt, using born level leptons. The cross sections are normalized by the bin width. The first bin (0-1 GeV) is not shown as large differences were observed in aMC@NLO and POWHEG predictions at Born level.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of $\phi^{\scriptscriptstyle *}_\eta$ in the dimuon final state.

The measured absolute cross sections in bins of $\phi^{\scriptscriptstyle *}_\eta$, using born level leptons. The cross sections are normalized by the bin width.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dimuon final state.

The measured absolute cross sections in bins of Z pt different |y(Z)| bins, using born level leptons. The cross sections are normalized by the bin width. The first bin (0-1 GeV) is not shown as large differences were observed in aMC@NLO and POWHEG predictions at Born level.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dimuon final state.

The measured normalized cross sections in bins of Z pt, using born level leptons. The first bin (0-1 GeV) is not shown as large differences were observed in aMC@NLO and POWHEG predictions at Born level.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dimuon final state.

The measured normalized cross sections in bins of $\phi^{\scriptscriptstyle *}_\eta$, using born level leptons.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dimuon final state.

The measured normalized cross sections in bins of |y(Z)|, using born level leptons.

The measured normalized cross sections (left) in bins of Z pt in |y(Z)| bins, using born level leptons. The first bin (0-1 GeV) is not shown as large differences were observed in aMC@NLO and POWHEG predictions at Born level.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dimuon final state.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt in the dielectron final state.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of |y(Z)| in the dielectron final state.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of $\phi^{\scriptscriptstyle *}_\eta$ in the dielectron final state.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dielectron final state.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dielectron final state.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dielectron final state.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dielectron final state.

Covariance matrix for normalized cross sections using dressed level leptons for all bins used in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dielectron final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt in the dimuon final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of |y(Z)| in the dimuon final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of $\phi^{\scriptscriptstyle *}_\eta$ in the dimuon final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dimuon final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dimuon final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dimuon final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dimuon final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dimuon final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt in the dielectron final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of |y(Z)| in the dielectron final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of $\phi^{\scriptscriptstyle *}_\eta$ in the dielectron final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 0 < |y(Z)| < 0.4 bin in the dielectron final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 0.4 < |y(Z)| < 0.8 bin in the dielectron final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 0.8 < |y(Z)| < 1.2 bin in the dielectron final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 1.2 < |y(Z)| < 1.6 bin in the dielectron final state.

Covariance matrix for normalized cross sections using born level leptons for all bins used in bins of Z pt for the 1.6 < |y(Z)| < 2.4 bin in the dielectron final state.

The expected and observed 95% CL upper limits on the product of cross section and branching fraction for direct production of charginos as a function of chargino mass, for a chargino lifetime of 333 ns and with a purely wino LSP. The branching fraction for $\widetilde{\chi}^{\pm}_{1} \rightarrow \widetilde{\chi}^{0}_{1} \pi^{\pm}$ is set to 100%. Shown are the full Run 2 results, derived from the results of the search in the 2017 and 2018 data sets combined with those of the previous CMS result obtained in the 2015 and 2016 data sets. The cross section includes both $\widetilde{\chi}^{\pm}_{1} \widetilde{\chi}^{0}_{1}$ and $\widetilde{\chi}^{\pm}_{1}\widetilde{\chi}^{\mp}_{1}$ production in roughly a 2:1 ratio for all chargino masses considered. The dashed line indicates the theoretical prediction.

The expected and observed 95% CL upper limits on the product of cross section and branching fraction for direct production of charginos as a function of chargino mass, for a chargino lifetime of 0.3 ns and with a purely higgsino LSP. The branching fractions are set to 95.5% for $\widetilde{\chi}^{\pm}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \pi^{\pm}$, 3% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} e\nu$, and 1.5% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \mu\nu$, with equal branching fractions and production cross sections between $\widetilde{\chi}^{0}_{1}$ and $\widetilde{\chi}^{0}_{2}$. The cross section includes both $\widetilde{\chi}^{\pm}_{1} \widetilde{\chi}^{0}_{1,2}$ and $\widetilde{\chi}^{\pm}_{1}\widetilde{\chi}^{\mp}_{1}$ production in roughly a 7:2 ratio for all chargino masses considered. The dashed line indicates the theoretical prediction.

The expected and observed 95% CL upper limits on the product of cross section and branching fraction for direct production of charginos as a function of chargino mass, for a chargino lifetime of 3.3 ns and with a purely higgsino LSP. The branching fractions are set to 95.5% for $\widetilde{\chi}^{\pm}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \pi^{\pm}$, 3% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} e\nu$, and 1.5% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \mu\nu$, with equal branching fractions and production cross sections between $\widetilde{\chi}^{0}_{1}$ and $\widetilde{\chi}^{0}_{2}$. The cross section includes both $\widetilde{\chi}^{\pm}_{1} \widetilde{\chi}^{0}_{1,2}$ and $\widetilde{\chi}^{\pm}_{1}\widetilde{\chi}^{\mp}_{1}$ production in roughly a 7:2 ratio for all chargino masses considered. The dashed line indicates the theoretical prediction.

The expected and observed 95% CL upper limits on the product of cross section and branching fraction for direct production of charginos as a function of chargino mass, for a chargino lifetime of 33 ns and with a purely higgsino LSP. The branching fractions are set to 95.5% for $\widetilde{\chi}^{\pm}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \pi^{\pm}$, 3% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} e\nu$, and 1.5% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \mu\nu$, with equal branching fractions and production cross sections between $\widetilde{\chi}^{0}_{1}$ and $\widetilde{\chi}^{0}_{2}$. The cross section includes both $\widetilde{\chi}^{\pm}_{1} \widetilde{\chi}^{0}_{1,2}$ and $\widetilde{\chi}^{\pm}_{1}\widetilde{\chi}^{\mp}_{1}$ production in roughly a 7:2 ratio for all chargino masses considered. The dashed line indicates the theoretical prediction.

The expected and observed 95% CL upper limits on the product of cross section and branching fraction for direct production of charginos as a function of chargino mass, for a chargino lifetime of 333 ns and with a purely higgsino LSP. The branching fractions are set to 95.5% for $\widetilde{\chi}^{\pm}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \pi^{\pm}$, 3% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} e\nu$, and 1.5% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \mu\nu$, with equal branching fractions and production cross sections between $\widetilde{\chi}^{0}_{1}$ and $\widetilde{\chi}^{0}_{2}$. The cross section includes both $\widetilde{\chi}^{\pm}_{1} \widetilde{\chi}^{0}_{1,2}$ and $\widetilde{\chi}^{\pm}_{1}\widetilde{\chi}^{\mp}_{1}$ production in roughly a 7:2 ratio for all chargino masses considered. The dashed line indicates the theoretical prediction.

The expected and observed constraints on chargino lifetime and mass for a purely wino LSP in the context of AMSB, where the chargino lifetime is explicitly varied. The chargino branching fraction is set to 100% for $\widetilde{\chi}^{\pm}_{1} \rightarrow \widetilde{\chi}^{0}_{1} \pi^{\pm}$. Shown are the full Run 2 results, derived from the results of the search in the 2017 and 2018 data sets combined with those of the previous CMS result obtained in the 2015 and 2016 data sets. The region to the left of the curve is excluded at 95% CL. The prediction from PLB 721 (2013) 252 is indicated in the dashed line.

The expected and observed 95% CL upper limits on the product of cross section and branching fraction for direct production of charginos as a function of chargino mass and mean proper lifetime, with a purely wino LSP. The branching fraction for $\widetilde{\chi}^{\pm}_{1} \rightarrow \widetilde{\chi}^{0}_{1} \pi^{\pm}$ is set to 100%. Shown are the full Run 2 results, derived from the results of the search in the 2017 and 2018 data sets combined with those of the previous CMS result obtained in the 2015 and 2016 data sets. The cross section includes both $\widetilde{\chi}^{\pm}_{1} \widetilde{\chi}^{0}_{1}$ and $\widetilde{\chi}^{\pm}_{1}\widetilde{\chi}^{\mp}_{1}$ production in roughly a 2:1 ratio for all chargino masses considered.

The expected and observed constraints on chargino lifetime and mass for a purely higgsino LSP in the context of AMSB, where the chargino lifetime is explicitly varied. The chargino branching fraction is set to 95.5% for $\widetilde{\chi}^{\pm}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \pi^{\pm}$, 3% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} e\nu$, and 1.5% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \mu\nu$, with equal branching fractions and production cross sections between $\widetilde{\chi}^{0}_{1}$ and $\widetilde{\chi}^{0}_{2}$. The region to the left of the curve is excluded at 95% CL. The prediction from Phys. Lett. B 781 (2018) 306 is indicated in the dashed line.

The expected and observed 95% CL upper limits on the product of cross section and branching fraction for direct production of charginos as a function of chargino mass and mean proper lifetime, with a purely higgsino LSP. The branching fractions are set to 95.5% for $\widetilde{\chi}^{\pm}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \pi^{\pm}$, 3% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} e\nu$, and 1.5% for $\widetilde{\chi}^{-}_{1} \rightarrow \widetilde{\chi}^{0}_{1,2} \mu\nu$, with equal branching fractions and production cross sections between $\widetilde{\chi}^{0}_{1}$ and $\widetilde{\chi}^{0}_{2}$. The cross section includes both $\widetilde{\chi}^{\pm}_{1} \widetilde{\chi}^{0}_{1,2}$ and $\widetilde{\chi}^{\pm}_{1}\widetilde{\chi}^{\mp}_{1}$ production in roughly a 7:2 ratio for all chargino masses considered.

Predicted signal yields for the 2017 data-taking period, corresponding to $41.5\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $700\,\text{GeV}{}$ in the case of a purely wino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2017 data-taking period, corresponding to $41.5\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $700\,\text{GeV}{}$ in the case of a purely higgsino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2017 data-taking period, corresponding to $41.5\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $300\,\text{GeV}{}$ in the case of a purely wino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2017 data-taking period, corresponding to $41.5\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $300\,\text{GeV}{}$ in the case of a purely higgsino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2018A data-taking period, corresponding to $21.1\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $700\,\text{GeV}{}$ in the case of a purely wino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2018A data-taking period, corresponding to $21.1\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $700\,\text{GeV}{}$ in the case of a purely higgsino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2018A data-taking period, corresponding to $21.1\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $300\,\text{GeV}{}$ in the case of a purely wino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2018A data-taking period, corresponding to $21.1\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $300\,\text{GeV}{}$ in the case of a purely higgsino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2018B data-taking period, corresponding to $38.6\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $700\,\text{GeV}{}$ in the case of a purely wino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2018B data-taking period, corresponding to $38.6\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $700\,\text{GeV}{}$ in the case of a purely higgsino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2018B data-taking period, corresponding to $38.6\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $300\,\text{GeV}{}$ in the case of a purely wino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Predicted signal yields for the 2018B data-taking period, corresponding to $38.6\,\text{fb}^{-1}$, after the application of each search requirement for three chargino lifetime hypotheses ($\tau = 0.3$, $3.3$, and $33\,\text{ns}{}$) with a chargino mass of $300\,\text{GeV}{}$ in the case of a purely higgsino neutralino LSP. Excepting the final three, the requirements listed are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. In the case of the final three requirements on the number of tracker layers with measurement, these are exclusive signal category selections where only one is applied for each category. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events, in the case of a purely wino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2017 data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events, in the case of a purely wino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2017 data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events, in the case of a purely higgsino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2017 data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1,2}$ events, in the case of a purely higgsino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1,2}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2017 data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events, in the case of a purely wino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2018A data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events, in the case of a purely wino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2018A data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events, in the case of a purely higgsino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2018A data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1,2}$ events, in the case of a purely higgsino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1,2}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2018A data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events, in the case of a purely wino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2018B data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events, in the case of a purely wino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2018B data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events, in the case of a purely higgsino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2018B data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Signal acceptance times efficiency ($A\times\epsilon$) for each of the generated chargino masses and lifetimes for $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1,2}$ events, in the case of a purely higgsino neutralino LSP. The denominator for the acceptance is the total number of $\widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1,2}$ events generated, while the numerator is the number of those events that are selected by the signal selection after being processed with the same reconstruction software used on the data. These $A\times\epsilon$ values correspond to the conditions simulated for the 2018B data-taking period. The uncertainties are only the statistical uncertainties resulting from the sizes of the generated samples.

Summary of the estimated backgrounds and the observation. The first and second uncertainties shown are the statistical and systematic contributions, respectively.

Detector-level photon $\phi$ distribution

Detector-level dipoton invariant mass distribution

Detector-level diphoton rapidity distribution

Detector-level dipoton p$_{T}$ distribution

Observed exclusion limits at 95% CL in the ALP-photon coupling g$_{a \gamma}$ versus ALP mass $m_{a}$ plane.

Covariance of the differential absolute cross section as a function of the parton-level top quark rapidity

Differential absolute cross section as a function of the parton-level charged lepton $p_\textrm{T}$

Covariance of the differential absolute cross section as a function of the parton-level charged lepton $p_\textrm{T}$

Differential absolute cross section as a function of the parton-level charged lepton rapidity

Covariance of the differential absolute cross section as a function of the parton-level charged lepton rapidity

Differential absolute cross section as a function of the parton-level W boson $p_\textrm{T}$

Covariance of the differential absolute cross section as a function of the parton-level W boson $p_\textrm{T}$

Differential absolute cross section as a function of the parton-level cosine of the top quark polarisation angle

Covariance of the differential absolute cross section as a function of the parton-level cosine of the top quark polarisation angle

Differential absolute cross section as a function of the particle-level top quark $p_\textrm{T}$

Covariance of the differential absolute cross section as a function of the particle-level top quark $p_\textrm{T}$

Differential absolute cross section as a function of the particle-level top quark rapidity

Covariance of the differential absolute cross section as a function of the particle-level top quark rapidity

Differential absolute cross section as a function of the particle-level charged lepton $p_\textrm{T}$

Covariance of the differential absolute cross section as a function of the particle-level charged lepton $p_\textrm{T}$

Differential absolute cross section as a function of the particle-level charged lepton rapidity

Covariance of the differential absolute cross section as a function of the particle-level charged lepton rapidity

Differential absolute cross section as a function of the particle-level W boson $p_\textrm{T}$

Covariance of the differential absolute cross section as a function of the particle-level W boson $p_\textrm{T}$

Differential absolute cross section as a function of the particle-level cosine of the top quark polarisation angle

Covariance of the differential absolute cross section as a function of the particle-level cosine of the top quark polarisation angle

Differential normalised cross section as a function of the parton-level top quark $p_\textrm{T}$

Covariance of the differential normalised cross section as a function of the parton-level top quark $p_\textrm{T}$

Differential normalised cross section as a function of the parton-level top quark rapidity

Covariance of the differential normalised cross section as a function of the parton-level top quark rapidity

Differential normalised cross section as a function of the parton-level charged lepton $p_\textrm{T}$

Covariance of the differential normalised cross section as a function of the parton-level charged lepton $p_\textrm{T}$

Differential normalised cross section as a function of the parton-level charged lepton rapidity

Covariance of the differential normalised cross section as a function of the parton-level charged lepton rapidity

Differential normalised cross section as a function of the parton-level W boson $p_\textrm{T}$

Covariance of the differential normalised cross section as a function of the parton-level W boson $p_\textrm{T}$

Differential normalised cross section as a function of the parton-level cosine of the top quark polarisation angle

Covariance of the differential normalised cross section as a function of the parton-level cosine of the top quark polarisation angle

Differential normalised cross section as a function of the particle-level top quark $p_\textrm{T}$

Covariance of the differential normalised cross section as a function of the particle-level top quark $p_\textrm{T}$

Differential normalised cross section as a function of the particle-level top quark rapidity

Covariance of the differential normalised cross section as a function of the particle-level top quark rapidity

Differential normalised cross section as a function of the particle-level charged lepton $p_\textrm{T}$

Covariance of the differential normalised cross section as a function of the particle-level charged lepton $p_\textrm{T}$

Differential normalised cross section as a function of the particle-level charged lepton rapidity

Covariance of the differential normalised cross section as a function of the particle-level charged lepton rapidity

Differential normalised cross section as a function of the particle-level W boson $p_\textrm{T}$

Covariance of the differential normalised cross section as a function of the particle-level W boson $p_\textrm{T}$

Differential normalised cross section as a function of the particle-level cosine of the top quark polarisation angle

Covariance of the differential normalised cross section as a function of the particle-level cosine of the top quark polarisation angle

Differential charge ratio as a function of the parton-level top quark $p_\textrm{T}$

Covariance of the differential charge ratio as a function of the parton-level top quark $p_\textrm{T}$

Differential charge ratio as a function of the parton-level top quark rapidity

Covariance of the differential charge ratio as a function of the parton-level top quark rapidity

Differential charge ratio as a function of the parton-level charged lepton $p_\textrm{T}$

Covariance of the differential charge ratio as a function of the parton-level charged lepton $p_\textrm{T}$

Differential charge ratio as a function of the parton-level charged lepton rapidity

Covariance of the differential charge ratio as a function of the parton-level charged lepton rapidity

Differential charge ratio as a function of the parton-level W boson $p_\textrm{T}$

Covariance of the differential charge ratio as a function of the parton-level W boson $p_\textrm{T}$

Differential charge ratio as a function of the particle-level top quark $p_\textrm{T}$

Covariance of the differential charge ratio as a function of the particle-level top quark $p_\textrm{T}$

Differential charge ratio as a function of the particle-level top quark rapidity

Covariance of the differential charge ratio as a function of the particle-level top quark rapidity

Differential charge ratio as a function of the particle-level charged lepton $p_\textrm{T}$

Covariance of the differential charge ratio as a function of the particle-level charged lepton $p_\textrm{T}$

Differential charge ratio as a function of the particle-level charged lepton rapidity

Covariance of the differential charge ratio as a function of the particle-level charged lepton rapidity

Differential charge ratio as a function of the particle-level W boson $p_\textrm{T}$

Covariance of the differential charge ratio as a function of the particle-level W boson $p_\textrm{T}$

Top quark spin asymmetry at the parton level in the muon and electron channel and their combination

The covariance matrix containing the statistical uncertainties of Figure 2a is shown.

The covariance matrix containing the experimental uncertainties of Figure 2a is shown.

The covariance matrix containing the model uncertainties of Figure 2a is shown.

The covariance matrix containing the statistical uncertainties of Figure 2b is shown.

The covariance matrix containing the experimental uncertainties of Figure 2b is shown.

The covariance matrix containing the model uncertainties of Figure 2b is shown.

Distribution of the resonance mass in the c-tag Signal Region of the $ ce$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the b-tag Signal Region of the $ ce$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the untagged Signal Region of the $ c\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the c-tag Signal Region of the $ c\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the b-tag Signal Region of the $ c\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 0-tag Signal Region of the $ be$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 1-tag Signal Region of the $ be$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 2-tag Signal Region of the $ be$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 0-tag Signal Region of the $ b\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 1-tag Signal Region of the $ b\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 2-tag Signal Region of the $ b\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into electrons, shown as a function of $m_{LQ}$ for the $qe$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into muons, shown as a function of $m_{LQ}$ for the $q\mu$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into electrons, shown as a function of $m_{LQ}$ for the $ce$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into muons, shown as a function of $m_{LQ}$ for the $c\mu$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into electrons, shown as a function of $m_{LQ}$ for the $be$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into muons, shown as a function of $m_{LQ}$ for the $b\mu$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $qe$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $q\mu$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $ce$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $c\mu$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $be$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $b\mu$ channel.

The signal selection efficiency x acceptance summed over all signal regions, for all masses and LQ decay channels considered.

The observed and expected limits for all masses and LQ decay channels considered.

Cutflow Table in the electron channel, considering signal samples with LQ mass of 1 TeV.

Cutflow Table in the muon channel, considering signal samples with LQ mass of 1 TeV.

Summary of results for charm and bottom muon v2 as a function of pT. Uncertainties are statistical and systematic, respectively.

Ratio of unfolded width of azimuthal angular correlation distributions (P PB/ P P). Different colors correspond to different combinations of p_{T,1} and p_{T,2}

Ratio of unfolded Dijet conditional yields (P PB/ P P). Different colors correspond to different combinations of p_{T,1} and p_{T,2}

Unfolded width of azimuthal angular correlation distributions (Delta p_{T} > 3). Full markers represent p+Pb, open markers p+p

Unfolded Dijet conditional yields (Delta p_{T} > 3). Full markers represent p+Pb, open markers p+p

Ratio of unfolded width of azimuthal angular correlation distributions (P PB/ P P) (Delta p_{T} > 3). Different colors correspond to different combinations of p_{T,1} and p_{T,2}

Ratio of unfolded Dijet conditional yields (P PB/ P P) (Delta p_{T} > 3). Different colors correspond to different combinations of p_{T,1} and p_{T,2}

Unfolded azimuthal angular correlation distributions. Black markers represent p+Pb, red markers p+p

Unfolded azimuthal angular correlation distributions (Delta p_{T} > 3). Black markers represent p+Pb, red markers p+p

Observed $m_{t\bar{t}}^{reco}$ distributions in data for SR$2b$, shown together with the result of the fit with the three-shape-parameter function. The error bars indicate the effect of the fit parameter uncertainty on the background prediction. The bin width of the distributions is chosen to be the same as that used in the background parameterization.

Observed and expected upper limits on the cross-section times branching fraction of the $Z'_{TC2} \rightarrow t\bar{t}$ as a function of the $Z'_{TC2}$ mass ($\Gamma/m = 1.2$%).

Fit parameter values obtained from fits to data in the two signal regions. The fit function is $f(x) = p_{0} (1-x)^{p_{1}} x^{p_{2} + p_{3} \log(x)}$, where $x = m^{reco}_{t\bar{t}}/\sqrt{s}$.

Post-fit BDT distributions after the VBF event selection for the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ analysis channel. The VBF signal is shown for $\mu = 0.73$ and $\tilde d = -0.01$. ''Other bkg'' denotes all background contributions not listed explicitly in the legend. The size of the combined statistical, experimental, and theoretical uncertainties is given.

Post-fit $m_{\tau\tau}^{\mathrm{MMC}}$ distributions in the low BDT score CR for the $\tau_{\mathrm{lep}}\tau_{\mathrm{lep}}$ SF analysis channel. ''Other bkg'' denotes all background contributions not listed explicitly in the legend. The size of the combined statistical, experimental, and theoretical uncertainties is given. The exact value of the $p_{\mathrm{T}}$ cut on the leptons depends on the trigger.

Post-fit $m_{\tau\tau}^{\mathrm{MMC}}$ distributions in the low BDT score CR for the $\tau_{\mathrm{lep}}\tau_{\mathrm{lep}}$ DF analysis channel. ''Other bkg'' denotes all background contributions not listed explicitly in the legend. The size of the combined statistical, experimental, and theoretical uncertainties is given.

Post-fit $m_{\tau\tau}^{\mathrm{MMC}}$ distributions in the low BDT score CR for the $\tau_{\mathrm{lep}}\tau_{\mathrm{had}}$ analysis channel. ''Other bkg'' denotes all background contributions not listed explicitly in the legend. The size of the combined statistical, experimental, and theoretical uncertainties is given. The exact value of the $p_{\mathrm{T}}$ cut on the leading lepton depends on the trigger.

Post-fit $m_{\tau\tau}^{\mathrm{MMC}}$ distributions in the low BDT score CR for the $\tau_{\mathrm{had}}\tau_{\mathrm{had}}$ analysis channel. ''Other bkg'' denotes all background contributions not listed explicitly in the legend. The size of the combined statistical, experimental, and theoretical uncertainties is given.