Mean $p_T$ values of the $\Upsilon(1$S$)$, $\Upsilon(2$S$)$, and $\Upsilon(3$S$)$ states with $p_T\,>\,0\,GeV$ and $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$

Ratio $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $0<p_T<5\,$GeV range

Ratio $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $5<p_T<7\,$GeV range

Ratio $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $7<p_T<9\,$GeV range

Ratio $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $9<p_T<11\,$GeV range

Ratio $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $11<p_T<15\,$GeV range

Ratio $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $15<p_T<20\,$GeV range

Ratio $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $20<p_T<50\,$GeV range

Ratio $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $0<p_T<5\,$GeV range

Ratio $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $5<p_T<7\,$GeV range

Ratio $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $7<p_T<9\,$GeV range

Ratio $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $9<p_T<11\,$GeV range

Ratio $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $11<p_T<15\,$GeV range

Ratio $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $15<p_T<20\,$GeV range

Ratio $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ with $|y|\,<\,1.2$ as a function of track multiplicity $N_{track}$ in $20<p_T<50\,$GeV range

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of "forward" track multiplicity $N_{track}^{\Delta\phi}$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$. Forward tracks are those with momentum direction in $\Delta\phi\,<\,\pi/3$ w.r.t. the $\Upsilon(n$S$)$ momentum direction.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of "transverse" track multiplicity $N_{track}^{\Delta\phi}$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$. Transverse tracks are those with momentum direction in $\pi/3\,<\,\Delta\phi\,<\,2\pi/3$ w.r.t. the $\Upsilon(n$S$)$ momentum direction.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of "backward" track multiplicity $N_{track}^{\Delta\phi}$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$. Backward tracks are those with momentum direction in $\Delta\phi\,>\,2\pi/3$ w.r.t. the $\Upsilon(n$S$)$ momentum direction.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of track multiplicity $N_{track}$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$ and $N^{\Delta R}_{track}\,=\,0$ charged particles produced in $\Delta R\,<\,0.5$ cone around $\Upsilon(n$S$)$ momentum direction.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of track multiplicity $N_{track}$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$ and $N^{\Delta R}_{track}\,=\,1$ charged particles produced in $\Delta R\,<\,0.5$ cone around $\Upsilon(n$S$)$ momentum direction.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of track multiplicity $N_{track}$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$ and $N^{\Delta R}_{track}\,=\,2$ charged particles produced in $\Delta R\,<\,0.5$ cone around $\Upsilon(n$S$)$ momentum direction.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of track multiplicity $N_{track}$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$ and $N^{\Delta R}_{track}\,>\,2$ charged particles produced in $\Delta R\,<\,0.5$ cone around $\Upsilon(n$S$)$ momentum direction.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of track multiplicity $N_{track}$ in transverse sphericity bin $0.00\,<\,S_T\,<\,0.55$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of track multiplicity $N_{track}$ in transverse sphericity bin $0.55\,<\,S_T\,<\,0.70$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of track multiplicity $N_{track}$ in transverse sphericity bin $0.70\,<\,S_T\,<\,0.85$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$.

Ratios $\Upsilon(2$S$)\,/\,\Upsilon(1$S$)$ and $\Upsilon(3$S$)\,/\,\Upsilon(1$S$)$ as functions of track multiplicity $N_{track}$ in transverse sphericity bin $0.85\,<\,S_T\,<\,1.00$ for $\Upsilon(n$S$)$ states with $p_T\,>\,7\,GeV$ and $|y|\,<\,1.2$.

Efficiency-corrected multiplicity bins used in the $\Upsilon(n$S$)$ ratio analysis and the corresponding mean number of charged particle tracks.

The correlation matrix for the njet measurement. The last bin is inclusive.

The fiducial integrated signal strength and cross section The uncertainty breakdown is given in terms of statistical (stat), experimental (exp), theoretical uncertainties on the background (bkg) and on the signal (sig), and the luminosity uncertainty (lumi). The fiducial cross section and its full uncertainty in each bin are also given.

Measured cross sections obtained with the BDT-based analyses. The uncertainties listed are statistical and systematic. For the results listed in the left (right) half of the table, Higgs boson contributions are counted as signal (background). The VVV cross section is calculated from the fit for \mu_{comb}. For ZZZ production, 95% confidence level upper limits are reported.

$v_{2}$ of $\Upsilon(\mathrm{1S})$ as a function of $p_{\mathrm{T}}$ in three centrality ranges, 10-30%, 30-50% and 50-90%.

$v_{2}$ of $\Upsilon(\mathrm{1S})$ as a function of $p_{\mathrm{T}}$ in 5-60% centrality range.

Charged particle yield distributions $Y(\Delta r)$ of b jets with $1 < p_{\text{T}}^{\text{trk}} < 12$ GeV are presented as functions of $\Delta r$.b jets with $p_T > 120$ GeV and charged particles with $1 < p^{\text{trk}}_{\text{T}} < 12$ GeV are used to construct the distributions as functions of $\Delta r$ differential $p_{\text{T}}^{\text{trk}}$ bins.

Charged particle yield differences of b to inclusive jets for $1 < p_{\text{T}}^{\text{trk}} < 12$ GeV are presented as functions of $\Delta r$.b jets with $p_T > 120$ GeV and charged particles with $1 < p^{\text{trk}}_{\text{T}} < 12$ GeV are used to construct the distributions as functions of $\Delta r$ differential $p_{\text{T}}^{\text{trk}}$ bins.

The jet shapes $\rho(\Delta r)$ of inclusive jets with $p_{\text{T}} > 120$ GeV and $p^{trk}_T > 1$ GeV are presented as functions of $\Delta r$ for differential $p_{\text{T}}^{\text{trk}}$ bin.

The jet shape distributions $\rho(\Delta r)$ of b jets with $p_{\text{T}} > 120$ GeV and $1< p^{\text{trk}}_{\text{T}} < 12$ GeV are presented as functions of $\Delta r$ for differential $p_{\text{T}}^{\text{trk}}$ bin.

The jet shapes $\rho (\Delta r)$ of inclusive jets with $p_{\text{T}} > 120$ GeV and $p^{\text{trk}}_{\text{T}} > 1$ GeV are presented as functions of $\Delta r.

The jet shapes $\rho (\Delta r)$ of b jets with $p_{\text{T}} > 120$ GeV and $p^{\text{trk}}_{\text{T}} > 1$ GeV are presented as functions of $\Delta r.

The b-to-inclusive jet shape ratios for $p_{\text{T}} > 120$ GeV and $p^{\text{trk}}_{\text{T}} > 1$ GeV are presented as functions of $\Delta r.

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.

Expected and observed yields from the standard model processeses in the WW signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WW signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WW signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WZ signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WZ signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

Expected and observed yields from the standard model processeses in the WZ signal region. The combination of the statistical and systematic uncertainties are shown. The predicted yields are shown with their best-fit normalizations from the simultaneous fit.

The measured inclusive fiducial cross section measurements. The WW fiducial region is defined by requiring two same-sign leptons with $p_{T}>20$, $|\eta|<2.5$, and $m_{ll}>20$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$. The jets at generator level are clustered from stable particles, excluding neutrinos, using the anti-kt clustering algorithm with R = 0.4, and are required to have $p_{T}>50$ and $|\eta|<4.7$. The jets within $\Delta R<0.4$ of the selected charged leptons are not included. The WZ fiducial region is defined by requiring three leptons with $p_{T}>20$, $|\eta|<2.5$, a pair of opposite charge same-flavor lepton pair with $|m_{ll}-m_{Z}|<15$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$.

The measured inclusive fiducial cross section measurements. The WW fiducial region is defined by requiring two same-sign leptons with $p_{T}>20$, $|\eta|<2.5$, and $m_{ll}>20$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$. The jets at generator level are clustered from stable particles, excluding neutrinos, using the anti-kt clustering algorithm with R = 0.4, and are required to have $p_{T}>50$ and $|\eta|<4.7$. The jets within $\Delta R<0.4$ of the selected charged leptons are not included. The WZ fiducial region is defined by requiring three leptons with $p_{T}>20$, $|\eta|<2.5$, a pair of opposite charge same-flavor lepton pair with $|m_{ll}-m_{Z}|<15$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$.

The measured inclusive fiducial cross section measurements. The WW fiducial region is defined by requiring two same-sign leptons with $p_{T}>20$, $|\eta|<2.5$, and $m_{ll}>20$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$. The jets at generator level are clustered from stable particles, excluding neutrinos, using the anti-kt clustering algorithm with R = 0.4, and are required to have $p_{T}>50$ and $|\eta|<4.7$. The jets within $\Delta R<0.4$ of the selected charged leptons are not included. The WZ fiducial region is defined by requiring three leptons with $p_{T}>20$, $|\eta|<2.5$, a pair of opposite charge same-flavor lepton pair with $|m_{ll}-m_{Z}|<15$, and two jets with $m_{jj}>500$ and $|\Delta \eta_{jj}|>2.5$.

Distributions of $m_{jj}$ in the WW signal region.

Distributions of $m_{jj}$ in the WW signal region.

Distributions of $m_{jj}$ in the WW signal region.

Distributions of $m_{ll}$ in the WW signal region.

Distributions of $m_{ll}$ in the WW signal region.

Distributions of $m_{ll}$ in the WW signal region.

Distributions of $m_{jj}$ in the WZ signal region.

Distributions of $m_{jj}$ in the WZ signal region.

Distributions of $m_{jj}$ in the WZ signal region.

Distributions of BDT score in the WZ signal region.

Distributions of BDT score in the WZ signal region.

Distributions of BDT score in the WZ signal region.

Absolute WW cross section in $m_{jj}$ bins.

Absolute WW cross section in $m_{jj}$ bins.

Absolute WW cross section in $m_{jj}$ bins.

Normalized WW cross section in $m_{jj}$ bins.

Normalized WW cross section in $m_{jj}$ bins.

Normalized WW cross section in $m_{jj}$ bins.

Absolute WW cross section in $m_{ll}$ bins.

Absolute WW cross section in $m_{ll}$ bins.

Absolute WW cross section in $m_{ll}$ bins.

Normalized WW cross section in $m_{ll}$ bins.

Normalized WW cross section in $m_{ll}$ bins.

Normalized WW cross section in $m_{ll}$ bins.

Absolute WW cross section in $p_{T}^{l max}$ bins.

Absolute WW cross section in $p_{T}^{l max}$ bins.

Absolute WW cross section in $p_{T}^{l max}$ bins.

Normalized WW cross section in $p_{T}^{l max}$ bins.

Normalized WW cross section in $p_{T}^{l max}$ bins.

Normalized WW cross section in $p_{T}^{l max}$ bins.

Absolute WZ cross section in $m_{jj}$ bins.

Absolute WZ cross section in $m_{jj}$ bins.

Absolute WZ cross section in $m_{jj}$ bins.

Normalized WZ cross section in $m_{jj}$ bins.

Normalized WZ cross section in $m_{jj}$ bins.

Normalized WZ cross section in $m_{jj}$ bins.

Distributions of $m_{T}^{WW}$ in the WW signal region.

Distributions of $m_{T}^{WW}$ in the WW signal region.

Distributions of $m_{T}^{WW}$ in the WW signal region.

Distributions of $m_{T}^{WZ}$ in the WZ signal region.

Distributions of $m_{T}^{WZ}$ in the WZ signal region.

Distributions of $m_{T}^{WZ}$ in the WZ signal region.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic, obtained without using any unitarization procedure.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic, obtained without using any unitarization procedure.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic, obtained without using any unitarization procedure.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic by cutting the EFT expansion at the unitarity limit.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic by cutting the EFT expansion at the unitarity limit.

Observed and expected lower and upper 95\% confidence level limits in TeV$^{-4}$ on the parameters of the quartic by cutting the EFT expansion at the unitarity limit.

The $m_{W}^{T}$ distribution from data (points) and simulation (shaded histograms) in the 2j1t (left) and 3j1t (right) categories for the muon (upper) and electron (lower) channels. The vertical lines on the points and the hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 2j1t category: the $|\eta|$ of the non-b-tagged jet $|\eta_{j^{'}}|$ (left) and the invariant mass of lepton and b jet momenta system (right), shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points and the hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distributionfrom the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 2j1t category: the $|\eta|$ of the non-b-tagged jet $|\eta_{j^{'}}|$ (left) and the invariant mass of lepton and b jet momenta system (right), shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points and the hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distributionfrom the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 2j1t category: the $|\eta|$ of the non-b-tagged jet $|\eta_{j^{'}}|$ (left) and the invariant mass of lepton and b jet momenta system (right), shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points and the hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distributionfrom the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 2j1t category: the $|\eta|$ of the non-b-tagged jet $|\eta_{j^{'}}|$ (left) and the invariant mass of lepton and b jet momenta system (right), shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points and the hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distributionfrom the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 3j1t category: the $p_{T}^{miss}$ in the transverse plane (left) and the value of the MVA b tagger discriminator when applied to the extra jet (right) are shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points andthe hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 3j1t category: the $p_{T}^{miss}$ in the transverse plane (left) and the value of the MVA b tagger discriminator when applied to the extra jet (right) are shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points andthe hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 3j1t category: the $p_{T}^{miss}$ in the transverse plane (left) and the value of the MVA b tagger discriminator when applied to the extra jet (right) are shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points andthe hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 3j1t category: the $p_{T}^{miss}$ in the transverse plane (left) and the value of the MVA b tagger discriminator when applied to the extra jet (right) are shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points andthe hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 3j2t category: the $|\eta|$ of the non-b-tagged jet $|\eta_{j^{'}}|$ (left) and the invariant mass of lepton and non-b-tagged jet system (right) are shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points and the hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 3j2t category: the $|\eta|$ of the non-b-tagged jet $|\eta_{j^{'}}|$ (left) and the invariant mass of lepton and non-b-tagged jet system (right) are shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points and the hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 3j2t category: the $|\eta|$ of the non-b-tagged jet $|\eta_{j^{'}}|$ (left) and the invariant mass of lepton and non-b-tagged jet system (right) are shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points and the hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distributions of the two most discriminating variables from data (points) and simulation (shaded histograms) in the 3j2t category: the $|\eta|$ of the non-b-tagged jet $|\eta_{j^{'}}|$ (left) and the invariant mass of lepton and non-b-tagged jet system (right) are shown for the muon (upper) and electron (lower) channels, respectively. The vertical lines on the points and the hatched bands show the experimental and MC statistical uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the MC prediction.

Distribution of the multivariate discriminators, comparing data to simulation normalised after the fit procedure, for the muon channel on the left and for the electron channel on the right, for 2j1t (upper), 3j1t (middle), and 3j2t (lower). The vertical lines on the points and the hatched bands show the experimental and fit uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the fit.

Distribution of the multivariate discriminators, comparing data to simulation normalised after the fit procedure, for the muon channel on the left and for the electron channel on the right, for 2j1t (upper), 3j1t (middle), and 3j2t (lower). The vertical lines on the points and the hatched bands show the experimental and fit uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the fit.

Distribution of the multivariate discriminators, comparing data to simulation normalised after the fit procedure, for the muon channel on the left and for the electron channel on the right, for 2j1t (upper), 3j1t (middle), and 3j2t (lower). The vertical lines on the points and the hatched bands show the experimental and fit uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the fit.

Distribution of the multivariate discriminators, comparing data to simulation normalised after the fit procedure, for the muon channel on the left and for the electron channel on the right, for 2j1t (upper), 3j1t (middle), and 3j2t (lower). The vertical lines on the points and the hatched bands show the experimental and fit uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the fit.

Distribution of the multivariate discriminators, comparing data to simulation normalised after the fit procedure, for the muon channel on the left and for the electron channel on the right, for 2j1t (upper), 3j1t (middle), and 3j2t (lower). The vertical lines on the points and the hatched bands show the experimental and fit uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the fit.

Distribution of the multivariate discriminators, comparing data to simulation normalised after the fit procedure, for the muon channel on the left and for the electron channel on the right, for 2j1t (upper), 3j1t (middle), and 3j2t (lower). The vertical lines on the points and the hatched bands show the experimental and fit uncertainties, respectively. The expected distribution from the STq,b+STb,q processes (multiplied by a factor of 1000) is shown by the solid blue line. The lower panels show the ratio of the data to the fit.

Values of the third-row elements of the CKM matrix inferred from low-energy measurements with the respective values of the top quark decay branching fractions. The q in bs{V_{tq}} and \mathcal{B}(t o Wq) in the first column refers to b, s, and d quarks, according to the quark label shown in the header row.

For each of the production and decay vertices, the cross section times branching fraction for the corresponding signal process from simulation. The uncertainties shown includethose from the factorisation and renormalisation scales, the PDFs, and any experimental uncer-tainties, where appropriate.

The sources and relative values in percent of the systematic uncertainty in the measurement of the STb,b cross section. The uncertainties are broken up into profiled and nonprofiled sources.

The signal strengths obtained under SM assumptions.

Results obtained under SM assumptions.

Results obtained under first BSM scenario.

Results obtained under second BSM scenario.

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 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 33 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 33 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 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 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 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 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 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 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 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 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 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.

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 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 $700\,\text{GeV}{}$ 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 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 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 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 $700\,\text{GeV}{}$ 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 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 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 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 $700\,\text{GeV}{}$ 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 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.

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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1,2}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1,2}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^0_{1}$ 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 $\mathrm{p}\mathrm{p} \rightarrow \widetilde{\chi}^\pm_{1} \widetilde{\chi}^\mp_{1}$ 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.

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.

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

The standard deviation of the jet charge distributions with different track $p_{T}$ thresholds and $\kappa$ value pf 0.7 for pp collisions and in the various event centrality bins for PbPb collisions compared with the PYTHIA6 prediction.

Fitting results for the extraction of gluon-like jet fractions in pp and PbPb data shown for different track $p_{T}$ threshold values and $p_{T}$-weighting factor $\kappa$ values of 0.5.

Fitting results for the extraction of gluon-like jet fractions in pp and PbPb data shown for $p_{T}$-weighting factor $\kappa$ values of 0.3, 0.5, and 0.7 and minimum track $p_{T}$ of 1 GeV.

Fitting results for the extraction of gluon-like jet fractions in pp and PbPb data shown for $p_{T}$-weighting factor $\kappa$ values of 0.3, 0.5, and 0.7 and minimum track $p_{T}$ of 2 GeV.

Unfolded jet charge measurements for the $p_{T}$-weighting factor $\kappa = 0.5$ and a minimum track $p_{T}$ of 2 GeV for inclusive jets in pp and PbPb data. The PbPb results are shown for different centrality regions.

Unfolded jet charge measurements for the $p_{T}$-weighting factor $\kappa = 0.5$ and a minimum track $p_{T}$ of 4 GeV for inclusive jets in pp and PbPb data. The PbPb results are shown for different centrality regions.

Unfolded jet charge measurements for the $p_{T}$-weighting factor $\kappa = 0.5$ and a minimum track $p_{T}$ of 5 GeV for inclusive jets in pp and PbPb data. The PbPb results are shown for different centrality regions.

Unfolded jet charge measurements for a minimum track $p_{T}$ threshold of 1 GeV and $p_{T}$-weighting factor $\kappa = 0.3$ for inclusive jets in pp and PbPb data. The PbPb results are shown for different centrality regions.

Unfolded jet charge measurements for a minimum track $p_{T}$ threshold of 1 GeV and $p_{T}$-weighting factor $\kappa = 0.7$ for inclusive jets in pp and PbPb data. The PbPb results are shown for different centrality regions.

Unfolded jet charge measurements for a minimum track $p_{T}$ threshold of 2 GeV and $p_{T}$-weighting factor $\kappa = 0.3$ for inclusive jets in pp and PbPb data. The PbPb results are shown for different centrality regions.

Unfolded jet charge measurements for a minimum track $p_{T}$ threshold of 2 GeV and $p_{T}$-weighting factor $\kappa = 0.7$ for inclusive jets in pp and PbPb data. The PbPb results are shown for different centrality regions.

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

constraint on CP-odd contribution at 68% CL

exclusion significance on pure CP odd contribution

Differential cross section as a function of the transverse momentum of the pion pair at 13 TeV, compared with generator-level simulations.

Differential cross section as a function of the rapidity of the pion pair at 5.02 TeV, compared with generator-level simulations.

Differential cross section as a function of the rapidity of the pion pair at 13 TeV, compared with generator-level simulations.

The fiducial cross section measured in bins of the azimuthal angle difference between the mesons for events in the fiducial region with 2 Y(1S) with absolute rapidity less than 2.0.

The expected and observed 95% CL limits on the product of the cross section and branching fraction for a tetraquark. The branching fraction is the product of the branching fraction for the decay of the resonance to a Y(1S) meson and two muons, and the Y(1S) meson dimuon branching fraction.

The expected and observed 95% CL limits on the product of the cross section and branching fraction for a scalar state. The branching fraction is the product of the branching fraction for the decay of the resonance to a Y(1S) meson and two muons, and the Y(1S) meson dimuon branching fraction.

The expected and observed 95% CL limits on the product of the cross section and branching fraction for a pseudoscalar state. The branching fraction is the product of the branching fraction for the decay of the resonance to a Y(1S) meson and two muons, and the Y(1S) meson dimuon branching fraction.

The expected and observed 95% CL limits on the product of the cross section and branching fraction for a spin-2 state. The branching fraction is the product of the branching fraction for the decay of the resonance to a Y(1S) meson and two muons, and the Y(1S) meson dimuon branching fraction.

Kinematic properties and charges of the four muons selected in data in the signal region for the measurement of the YY production cross section. MUON1 and MUON2 form the dimuon pair associated to the dimuon object required at trigger-level. The invariant mass of both muon pairs is between 8.6 and 11 GeV. All events pass the trigger requirements and have four muons with a total charge of 0 and with vertex probability above 5%. The muons have pT above 3.5 GeV if they have an absolute pseudorapidity below 0.9 and 2.5 GeV otherwise. The muons pass the medium muon identification criterion.

Exclusion regions at 95% CL in the $m_{\tilde{\chi}_1^0}$ versus $m_{\tilde{g}}$ plane for the T1tttt (upper left) and T5ttbbWW (upper right) models, with off-shell third-generation squarks, and the T5tttt (lower left) and T5ttcc (lower right) models, with on-shell third-generation squarks. For the T5ttbbWW model, $m_{\tilde{\chi}_1^\pm} = m_{\tilde{\chi}_1^0} + 5 GeV$, for the T5tttt model, $m_{\tilde{t}} - m_{\tilde{\chi}_1^0} = m_t$, and for the T5ttcc model, $m_{\tilde{t}} - m_{\tilde{\chi}_1^0} = 20 GeV$ and the decay proceeds through $\tilde{t} \to c \tilde{\chi}_1^0$. The right-hand side color scale indicates the excluded cross section values for a given point in the SUSY particle mass plane. The solid black curves represent the observed exclusion limits assuming the approximate-NNLO+NNLL cross sections (thick line), or their variations of $\pm 1$ standard deviations (s.d.) (thin lines). The dashed red curves show the expected limits with the corresponding $\pm 1$ s.d. and $\pm 2$ s.d. uncertainties. Excluded regions are to the left and below the limit curves.

Exclusion regions at 95% CL in the plane of $m_{\tilde{\chi}_1^0}$ versus $m_{\tilde{g}}$ for the T5qqqqWZ model with $m_{\tilde{\chi}_1^\pm}=0.5(m_{\tilde{g}} + m_{\tilde{\chi}_1^0})$~(left) and with $m_{\tilde{\chi}_1^\pm} = m_{\tilde{\chi}_1^0} + 20 GeV$~(right). The notations are as in Fig. 7.

Exclusion regions at 95% CL in the plane of $m_{\tilde{\chi}_1^0}$ versus $m_{\tilde{g}}$ for the T5qqqqWZ model with $m_{\tilde{\chi}_1^\pm}=0.5(m_{\tilde{g}} + m_{\tilde{\chi}_1^0})$~(left) and with $m_{\tilde{\chi}_1^\pm} = m_{\tilde{\chi}_1^0} + 20 GeV$~(right). The notations are as in Fig. 7.

Exclusion regions at 95% CL in the plane of $m_{\tilde{\chi}_1^0}$ versus $m_{\tilde{g}}$ for the T5qqqqWW model with $m_{\tilde{\chi}_1^\pm}=0.5(m_{\tilde{g}} + m_{\tilde{\chi}_1^0})$~(left) and with $m_{\tilde{\chi}_1^\pm} = m_{\tilde{\chi}_1^0} + 20 GeV$~(right). The notations are as in Fig. 7.

Exclusion regions at 95% CL in the plane of $m_{\tilde{\chi}_1^0}$ versus $m_{\tilde{g}}$ for the T5qqqqWW model with $m_{\tilde{\chi}_1^\pm}=0.5(m_{\tilde{g}} + m_{\tilde{\chi}_1^0})$~(left) and with $m_{\tilde{\chi}_1^\pm} = m_{\tilde{\chi}_1^0} + 20 GeV$~(right). The notations are as in Fig. 7.

Exclusion regions at 95% CL in the plane of $m_{\tilde{\chi}_1^\pm}$ versus $m_{\tilde{b}_1}$ for the T6ttWW model with $m_{\tilde{\chi}_1^0}=50 GeV$. The notations are as in Fig. 7.

Exclusion regions at 95% CL in the plane of $m(\tilde{t}_1)$ versus $m(\tilde{t}_2)$ for the T6ttHZ model with $m(\tilde{t}_1)-m(\tilde{\chi}_1^0)=175 GeV$. The three exclusions represent $\mathcal{B}(\tilde{t}_2\to\tilde{t}_1 Z)$ of 0, 50, and 100%, respectively. The notations are as in Fig. 7.

Exclusion regions at 95% CL in the plane of $m(\tilde{t}_1)$ versus $m(\tilde{t}_2)$ for the T6ttHZ model with $m(\tilde{t}_1)-m(\tilde{\chi}_1^0)=175 GeV$. The three exclusions represent $\mathcal{B}(\tilde{t}_2\to\tilde{t}_1 Z)$ of 0, 50, and 100%, respectively. The notations are as in Fig. 7.

Exclusion regions at 95% CL in the plane of $m(\tilde{t}_1)$ versus $m(\tilde{t}_2)$ for the T6ttHZ model with $m(\tilde{t}_1)-m(\tilde{\chi}_1^0)=175 GeV$. The three exclusions represent $\mathcal{B}(\tilde{t}_2\to\tilde{t}_1 Z)$ of 0, 50, and 100%, respectively. The notations are as in Fig. 7.

Upper limits at 95% CL on the cross section for RPV gluino pair production with each gluino decaying into four quarks and one lepton (T1qqqqL, left), and each gluino decaying into a top, bottom, and strange quarks (T1tbs, right).

Upper limits at 95% CL on the cross section for RPV gluino pair production with each gluino decaying into four quarks and one lepton (T1qqqqL, left), and each gluino decaying into a top, bottom, and strange quarks (T1tbs, right).

Upper limits at 95% CL on the product of cross section, detector acceptance, and selection efficiency, $\sigma \mathcal{A} \epsilon$, for the production of an SS lepton pair with at least two jets, as a function of the minimum $p_\mathrm{T}^\mathrm{miss}$ threshold, when $H_\mathrm{T}>300 GeV$ (left), or the minimum $H_\mathrm{T}$ threshold, when $p_\mathrm{T}^\mathrm{miss}<300 GeV$ (right).

Upper limits at 95% CL on the product of cross section, detector acceptance, and selection efficiency, $\sigma \mathcal{A} \epsilon$, for the production of an SS lepton pair with at least two jets, as a function of the minimum $p_\mathrm{T}^\mathrm{miss}$ threshold, when $H_\mathrm{T}>300 GeV$ (left), or the minimum $H_\mathrm{T}$ threshold, when $p_\mathrm{T}^\mathrm{miss}<300 GeV$ (right).

Unfolded R(b/j) cross section ratio versus Z boson transverse momentum

Unfolded R(c/b) cross section ratio versus jet transverse momentum

Unfolded R(c/b) cross section ratio versus Z boson transverse momentum

Total correlation matrix of the physics parameters of interest for the 8+13 TeV combination.

Cross section ratios as functions of the Bc pT.

Cross section ratios as functions of the Bc |y|.

Yield ratios of chi_c2 over chi_c1 mesons as a function of costh (HX) in the J/psi pT range 8-12 GeV

Yield ratios of chi_c2 over chi_c1 mesons as a function of costh (HX) in the J/psi pT range 12-18 GeV

Yield ratios of chi_c2 over chi_c1 mesons as a function of costh (HX) in the J/psi pT range 18-30 GeV

The $L_{T}$ distribution in the MisID-enriched region. The last bin contains the overflow events.

The $L_{T}+p^{miss}_{T}$ distribution in the 3L below-Z signal region. The last bin contains the overflow events.

The $M_{T}$ distribution in the 3L on-Z signal region. The last bin contains the overflow events.

The $L_{T}+p^{miss}_{T}$ distribution in the 3L above-Z signal region. The last bin contains the overflow events.

The $L_{T}+p^{miss}_{T}$ distribution in the 3L OSSF0 signal region. The last bin contains the overflow events.

The $L_{T}+p^{miss}_{T}$ distribution in the 4L OSSF0 signal region. The last bin contains the overflow events.

The $L_{T}+p^{miss}_{T}$ distribution in the 4L OSSF1 signal region. The last bin contains the overflow events.

The $L_{T}+p^{miss}_{T}$ distribution in the 4L OSSF2 signal region. The last bin contains the overflow events.

The dielectron $M_{OSSF}^{20}$ distribution in the 3L(ee) 0B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{300}$ distribution in the 3L(ee) 0B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{20}$ distribution in the 3L(ee) 0B, 400<$S_{T}$<800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{300}$ distribution in the 3L(ee) 0B, 400<$S_{T}$<800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{20}$ distribution in the 3L(ee) 0B, $S_{T}$>800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{300}$ distribution in the 3L(ee) 0B, $S_{T}$>800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{20}$ distribution in the 3L(ee) 1B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{300}$ distribution in the 3L(ee) 1B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{20}$ distribution in the 3L(ee) 1B, 400<$S_{T}$<800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{300}$ distribution in the 3L(ee) 1B, 400<$S_{T}$<800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{20}$ distribution in the 3L(ee) 1B, $S_{T}$>800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{300}$ distribution in the 3L(ee) 1B, $S_{T}$>800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{20}$ distribution in the 4L(ee) 0B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{300}$ distribution in the 4L(ee) 0B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{20}$ distribution in the 4L(ee) 0B, $S_{T}$>400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{300}$ distribution in the 4L(ee) 0B, $S_{T}$>400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{20}$ distribution in the 4L(ee) 1B signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dielectron $M_{OSSF}^{300}$ distribution in the 4L(ee) 1B signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {ee})$=0.05.

The dimuon $M_{OSSF}^{20}$ distribution in the 3L($\mu\mu$) 0B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{300}$ distribution in the 3L($\mu\mu$) 0B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{20}$ distribution in the 3L($\mu\mu$) 0B, 400<$S_{T}$<800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{300}$ distribution in the 3L($\mu\mu$) 0B, 400<$S_{T}$<800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{20}$ distribution in the 3L($\mu\mu$) 0B, $S_{T}$>800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{300}$ distribution in the 3L($\mu\mu$) 0B, $S_{T}$>800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{20}$ distribution in the 3L($\mu\mu$) 1B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{300}$ distribution in the 3L($\mu\mu$) 1B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{20}$ distribution in the 3L($\mu\mu$) 1B, 400<$S_{T}$<800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{300}$ distribution in the 3L($\mu\mu$) 1B, 400<$S_{T}$<800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{20}$ distribution in the 3L($\mu\mu$) 1B, $S_{T}$>800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{300}$ distribution in the 3L($\mu\mu$) 1B, $S_{T}$>800 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{20}$ distribution in the 4L($\mu\mu$) 0B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{300}$ distribution in the 4L($\mu\mu$) 0B, $S_{T}$<400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{20}$ distribution in the 4L($\mu\mu$) 0B, $S_{T}$>400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{300}$ distribution in the 4L($\mu\mu$) 0B, $S_{T}$>400 GeV signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{20}$ distribution in the 4L($\mu\mu$) 1B signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The dimuon $M_{OSSF}^{300}$ distribution in the 4L($\mu\mu$) 1B signal region. The last bin does not contain the overflow events. The signal is shown with $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$=0.05.

The 95% confidence level exclusion limits for the flavor-democratic scenario on the total production cross section of heavy fermion pairs.

The 95% confidence level exclusion limits on the product of the total production cross section of tt$\phi$(S) and $\mathcal{B}(\phi - {ee})$.

The 95% confidence level exclusion limits on the product of the total production cross section of tt$\phi$(PS) and $\mathcal{B}(\phi - {ee})$.

The 95% confidence level exclusion limits on the product of the total production cross section of tt$\phi$(S) and $\mathcal{B}(\phi - {\mu\mu})$.

The 95% confidence level exclusion limits on the product of the total production cross section of tt$\phi$(PS) and $\mathcal{B}(\phi - {\mu\mu})$.

The 95% confidence level exclusion limits on $g_{t}^2\mathcal{B}(\phi - {ee})$ for tt$\phi$(S).

The 95% confidence level exclusion limits on $g_{t}^2\mathcal{B}(\phi - {ee})$ for tt$\phi$(PS).

The 95% confidence level exclusion limits on $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$ for tt$\phi$(S).

The 95% confidence level exclusion limits on $g_{t}^2\mathcal{B}(\phi - {\mu\mu})$ for tt$\phi$(PS).

Product of the fiducial acceptance and the event selection efficiency for the type-III Seesaw signal model at various signal mass hypotheses calculated after all analysis selection requirements.

Product of the fiducial acceptance and the event selection efficiency for the tt$\phi$ signal models at various signal mass hypotheses calculated after all analysis selection requirements.

The 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for dijet resonances decaying to quark-quark. Limits are compared to predicted cross sections for axigluons, colorons, scalar diquarks, new gauge bosons W' and Z' with SM-like couplings and dark matter mediators.

The 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for dijet resonances decaying to quark-gluon. Limits are compared to predicted cross sections for string resonances and excited quarks.

The 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for dijet resonances decaying to gluon-gluon. Limits are compared to predicted cross section for color-octet scalars.

The 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for Randall–Sundrum gravitons. Limits are compared to the predicted cross section.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for spin-2 resonances produced and decaying in the quark-quark channel, shown for various values of intrinsic width as a function of resonance mass.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for spin-2 resonances produced and decaying in the gluon-gluon channel, shown for various values of intrinsic width as a function of resonance mass.

The observed 95% CL upper limits on the product of the cross section, branching fraction, and acceptance for spin-1 resonances produced and decaying in the quark-quark channel, shown for various values of intrinsic width as a function of resonance mass.

The normalized particle-level $\mathrm{t}\overline{\mathrm{t}}$ differential cross section in the fiducial region as a function of the XCone-jet mass.

The normalized particle-level $\mathrm{t}\overline{\mathrm{t}}$ differential cross section in the fiducial region as a function of the XCone-jet mass.

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

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 experimental uncertainties of Figure 2a is shown.

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

The covariance matrix containing the experimental 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.

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

The rho distributions for the mixed-flavour category events corresponding to a signal hypothesis with mH = 261 GeV and mA = 150 GeV.

The rho distributions for the same-flavour category events corresponding to a signal hypothesis with mH = 442 GeV and mA = 193 GeV. The signal is normalised to its theoretical cross section.

The rho distributions for the mixed-flavour category events corresponding to a signal hypothesis with mH = 442 GeV and mA = 193 GeV.

Expected and observed 95% CL upper limits on the product of the production cross section and branching fraction for H(A) -> ZA(H) -> bbbar as a function of mA and mH. The limits are computed using the asymptotic CLs method, combining the ee and μμ channels.

Expected and observed 95% CL upper limits on the signal strength for the Type-II 2HDM benchmark (tan(beta)=1.5, cos(beta-alpha)=0.01) as a function of mA and mH . The limits are computed using the asymptotic CLs method, combining the ee and μμ channels.

Expected and observed 95% CL upper limits on the signal strength for the Type-II 2HDM benchmark (mH = 379 GeV and m A = 172 GeV) as a function of tan(beta) and cos(beta-alpha). The limits are computed using the asymptotic CLs method, combining the ee and μμ channels.

P-value for H(A) -> ZA(H) -> bbbar production as a function of mA and mH. The limits are computed using the asymptotic CLs method, combining the ee and μμ channels.

Acceptance for a vector resonance decaying into a dijet as a function of the resonance mass. The acceptance is calculated using signal simulations for the analysis selection, namely three wide jets with $p_{\mathrm{T}}$ > 72 GeV and |$\eta$| < 2.5, and |$\eta_{1}$ − $\eta_{2}$|<1.1. The errors are dominated by the uncertainties in the parton shower modeling used in signal simulations.

Comparison of the dijet mass distribution of the nominal selection (black), control selection without correction (red), and control selection with correction (blue) for data (solid histograms) and a simulation of a signal from a resonance with a mass of 400 GeV (dashed histograms). The correction, applied as a function of the product of the two largest jet transverse momenta in the event, is obtained as the ratio of the numbers of events passing the signal selection to those passing the control selection. For ease of readability, the dijet mass distributions of the signal have been scaled by an arbitrary factor. The bottom plot shows the relative difference between the dijet mass distributions of the data from the control and nominal selections. The yield of the uncorrected control selection for data is 95.7% of the nominal selection. The overlap between the two data selections is about 35% of the nominal selection. The yield of the uncorrected control selection for signal simulation of a 400-GeV resonance is about 50% of the nominal selection.

Fraction of data and simulated signal events passing different stages of the analysis selection. The selection corresponding to each line includes the selection of the upper lines. Data are required to pass the trigger selection. The uncertainties quoted are statistical only.

Values of the predicted signal yields in each of the 15 signal regions (for $ x=0.75 $).

Pre-fit background-only covariance among different search bins.

Expected $95\%$ CL upper limit on the production cross section of top squarks (for $ x=0.25 $).

Top squark - neutralino mass points lying on the expected exclusion contour (for $ x=0.25 $).

Top squark - neutralino mass points lying on the expected-$1\sigma_\text{expected}$ exclusion contour (for $ x=0.25 $).

Top squark - neutralino mass points lying on the expected+$1\sigma_\text{expected}$ exclusion contour (for $ x=0.25 $).

Observed $95\%$ CL upper limit on the production cross section of top squarks (for $ x=0.25 $).

Top squark - neutralino mass points lying on the observed exclusion contour (for $ x=0.25 $).

Top squark - neutralino mass points lying on the observed-$1\sigma_\text{observed}$ exclusion contour (for $ x=0.25 $).

Top squark - neutralino mass points lying on the observed+$1\sigma_\text{observed}$ exclusion contour (for $ x=0.25 $).

Expected $95\%$ CL upper limit on the production cross section of top squarks (for $ x=0.5 $).

Top squark - neutralino mass points lying on the expected exclusion contour (for $ x=0.5 $).

Top squark - neutralino mass points lying on the expected-$1\sigma_\text{expected}$ exclusion contour (for $ x=0.5 $).

Top squark - neutralino mass points lying on the expected+$1\sigma_\text{expected}$ exclusion contour (for $ x=0.5 $).

Observed $95\%$ CL upper limit on the production cross section of top squarks (for $ x=0.5 $).

Top squark - neutralino mass points lying on the observed exclusion contour (for $ x=0.5 $).

Top squark - neutralino mass points lying on the observed-$1\sigma_\text{observed}$ exclusion contour (for $ x=0.5 $).

Top squark - neutralino mass points lying on the observed+$1\sigma_\text{observed}$ exclusion contour (for $ x=0.5 $).

Expected $95\%$ CL upper limit on the production cross section of top squarks (for $ x=0.75 $).

Top squark - neutralino mass points lying on the expected exclusion contour (for $ x=0.75 $).

Top squark - neutralino mass points lying on the expected-$1\sigma_\text{expected}$ exclusion contour (for $ x=0.75 $).

Top squark - neutralino mass points lying on the expected+$1\sigma_\text{expected}$ exclusion contour (for $ x=0.75 $).

Observed $95\%$ CL upper limit on the production cross section of top squarks (for $ x=0.75 $).

Top squark - neutralino mass points lying on the observed exclusion contour (for $ x=0.75 $).

Top squark - neutralino mass points lying on the observed-$1\sigma_\text{observed}$ exclusion contour (for $ x=0.75 $).

Top squark - neutralino mass points lying on the observed+$1\sigma_\text{observed}$ exclusion contour (for $ x=0.75 $).

The intercept parameter as a function of kT using the HCS method in pp collisions at 13 TeV.

1/Rinv^2 as a function of mT using the HCS method in pp collisions at 13 TeV.

The depth of the anticorrelation as function of multiplicity for kT integrated values.

Correlation function from the double ratio technique, integrated in the range 0 < kT < 1 GeV, for 0=<Ntrkoffline<=4. The error bars represent statistical uncertainties.

Correlation function from the double ratio technique, integrated in the range 0 < kT < 1 GeV, for 10=<Ntrkoffline<=12. The error bars represent statistical uncertainties.

Correlation function from the double ratio technique, integrated in the range 0 < kT < 1 GeV, for 31=<Ntrkoffline<=33. The error bars represent statistical uncertainties.

Correlation function from the double ratio technique, integrated in the range 0 < kT < 1 GeV, for 80=<Ntrkoffline<=84. The error bars represent statistical uncertainties.

Correlation function from the double ratio technique, integrated in the range 0 < kT < 1 GeV, for 105=<Ntrkoffline<=109. The error bars represent statistical uncertainties.

Correlation function from the double ratio technique, integrated in the range 0 < kT < 1 GeV, for 130=<Ntrkoffline<=250. The error bars represent statistical uncertainties.

Mixed higher-order flow harmonic $v_6\{\Psi_{33}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_7\{\Psi_{223}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_4\{\Psi_{22}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_5\{\Psi_{23}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{222}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{33}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_7\{\Psi_{223}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_4\{\Psi_{22}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_5\{\Psi_{23}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{222}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{33}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_7\{\Psi_{223}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_4\{\Psi_{22}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_5\{\Psi_{23}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{222}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{33}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_7\{\Psi_{223}\}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

The overall flow harmonic $v_4\{\Psi_{4}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

The overall flow harmonic $v_5\{\Psi_{5}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

The overall flow harmonic $v_6\{\Psi_{6}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

The overall flow harmonic $v_6\{\Psi_{6}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

The overall flow harmonic $v_7\{\Psi_{7}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

The overall flow harmonic $v_4\{\Psi_{4}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

The overall flow harmonic $v_5\{\Psi_{5}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

The overall flow harmonic $v_6\{\Psi_{6}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

The overall flow harmonic $v_6\{\Psi_{6}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

The overall flow harmonic $v_7\{\Psi_{7}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_4\{\Psi_{22}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_5\{\Psi_{23}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{222}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{33}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_7\{\Psi_{223}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Mixed higher-order flow harmonic $v_4\{\Psi_{22}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_5\{\Psi_{23}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{222}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_6\{\Psi_{33}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_7\{\Psi_{223}\}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{422}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{523}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{6222}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{633}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{7223}$ from the scalar-product method at 5.02 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{422}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{523}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{6222}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{633}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{7223}$ from the scalar-product method at 5.02 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{422}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{523}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{6222}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{633}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{7223}$ from the scalar-product method at 2.76 TeV as a function of PT in the 0-20% centrality range.

Nonlinear response coefficient $\chi_{422}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{523}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{6222}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{633}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

Nonlinear response coefficient $\chi_{7223}$ from the scalar-product method at 2.76 TeV as a function of PT in the 20-60% centrality range.

Mixed higher-order flow harmonic $v_4\{\Psi_{22}\}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Mixed higher-order flow harmonic $v_5\{\Psi_{23}\}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Mixed higher-order flow harmonic $v_6\{\Psi_{222}\}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Mixed higher-order flow harmonic $v_6\{\Psi_{33}\}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Mixed higher-order flow harmonic $v_7\{\Psi_{223}\}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Mixed higher-order flow harmonic $v_4\{\Psi_{22}\}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Mixed higher-order flow harmonic $v_5\{\Psi_{23}\}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Mixed higher-order flow harmonic $v_6\{\Psi_{222}\}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Mixed higher-order flow harmonic $v_6\{\Psi_{33}\}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Mixed higher-order flow harmonic $v_7\{\Psi_{223}\}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{422}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{523}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{6222}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{633}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{7223}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{422}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{523}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{6222}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{633}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{7223}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{422}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{523}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{6222}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{633}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{7223}$ from the scalar-product method at 5.02 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{422}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{523}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{6222}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{633}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Nonlinear response coefficient $\chi_{7223}$ from the scalar-product method at 2.76 TeV as a function of centrality.

Invariant $p_{T}$-differential spectra of $\Lambda + \bar{\Lambda}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

Invariant $p_{T}$-differential spectra of $\Xi- + \bar{\Xi+}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in various |$y_{CM}$| ranges

Invariant $p_{T}$-differential spectra of $\Xi- + \bar{\Xi+}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

Invariant $p_{T}$-differential spectra of $\Omega- + \bar{\Omega+}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

Invariant $p_{T}$-differential spectra of $\Omega- + \bar{\Omega+}$ in p+p and p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in -1.8 < $y_{CM}$ < 0

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0 < $y_{CM}$ < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

Invariant $p_{T}$-differential spectra of ${K_{0}}^{S}$ in p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in |$y_{CM}$| < 1.8

Invariant $p_{T}$-differential spectra of $\Lambda + \bar{\Lambda}$ in p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in -1.8 < $y_{CM}$ < 0

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.3 < $y_{CM}$ < 0.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in -1.8 < $y_{CM}$ < 0

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.8 < $y_{CM}$ < 1.3

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in -1.8 < $y_{CM}$ < 0

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 1.3 < $y_{CM}$ < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0 < $y_{CM}$ < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0 < $y_{CM}$ < 1.8

$R_{pPb}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0 < $y_{CM}$ < 1.8

Invariant $p_{T}$-differential spectra of ${K_{0}}^{S}$ in p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

Invariant $p_{T}$-differential spectra of $\Lambda + \bar{\Lambda}$ in p+Pb at $\sqrt{s}$=5.02 TeV in various $y_{CM}$ ranges

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.3 < |$y_{CM}$| < 0.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.3 < |$y_{CM}$| < 0.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.3 < |$y_{CM}$| < 0.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.8 < |$y_{CM}$| < 1.3

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.8 < |$y_{CM}$| < 1.3

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 0.8 < |$y_{CM}$| < 1.3

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 1.3 < |$y_{CM}$| < 1.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 1.3 < |$y_{CM}$| < 1.8

$Y_{asym}$ in p+Pb at $\sqrt{s}$=5.02 TeV in 1.3 < |$y_{CM}$| < 1.8

Experimental Xi values and FJF predictions for the four NRQCD terms using BK LDME parameters

Experimental Xi values and FJF predictions for the four NRQCD terms using BCKL LDME parameters

Experimental Xi values and FJF predictions for the four NRQCD terms using BK LDME parameters

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 cross sections are divided by the bin width and then the distributions are normalized to 1.

The measured normalized cross sections in bins of $\phi^{\scriptscriptstyle *}_\eta$, using dressed level leptons. The cross sections are divided by the bin width and then the distributions are normalized to 1.

The measured normalized cross sections in bins of |y(Z)|, using dressed level leptons. The cross sections are divided by the bin width and then the distributions are normalized to 1.

The measured normalized cross sections in bins of Z pt in |y(Z)| bins, using dressed level leptons. The cross sections are divided by the bin width and then the distributions are normalized to 1.

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.

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

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.

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.

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.

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.

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.

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

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.

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

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. The cross sections are divided by the bin width and then the distributions are normalized to 1.

The measured normalized cross sections in bins of $\phi^{\scriptscriptstyle *}_\eta$, using born level leptons. The cross sections are divided by the bin width and then the distributions are normalized to 1.

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. The cross sections are divided by the bin width and then the distributions are normalized to 1.

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

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

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

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

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

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

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

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

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

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

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

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

Exclusion limits at 95% CL for direct gluino pair production, where the gluinos decay to light-flavor quarks and a $\tilde{\chi}_1^\pm$ that decays to $W^\pm\tilde{\chi}_1^0$. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, assuming unity branching fraction to $q_i\bar{q}_j W\pm\tilde{\chi}_1^0$.

Exclusion limits at 95% CL for direct gluino pair production, where the gluinos decay to bottom quarks ($\tilde{g}\to b\bar{b}\tilde{\chi}_1^0$). Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, assuming unity branching fraction for the given decay.

Exclusion limits at 95% CL for direct gluino pair production, where the gluinos decay to top quarks ($\tilde{g}\to t\bar{t}\tilde{\chi}_1^0$). Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, assuming unity branching fraction for the given decay.

Exclusion limits at 95% CL for light-flavor squark pair production, where the squarks decay to $q\tilde{\chi}_1^0$. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, assuming unity branching fraction for the given decay and 1-fold degeneracy in the light-flavor squarks (corresponding to the inner set of curves in the limit plot). To get the theory cross section for other N-fold degeneracy assumptions (e.g. 8-fold for the outer curves in the limit plot), just multiply by N.

Exclusion limits at 95% CL for bottom squark pair production, where the squarks decay to $b\tilde{\chi}_1^0$. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, assuming unity branching fraction for the given decay.

Exclusion limits at 95% CL for top squark pair production, where the squarks decay to $t\tilde{\chi}_1^0$. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, assuming unity branching fraction for the given decay.

Exclusion limits at 95% CL for top squark pair production, where the squarks decay to $b\tilde{\chi}_1^\pm$ and the $\tilde{\chi}_1^0$ decay to $W^\pm\tilde{\chi}_1^0$. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, assuming unity branching fraction for the given decay.

Exclusion limits at 95% CL for top squark pair production, where the squarks decay either to $b\tilde{\chi}_1^\pm\to bW^\pm\tilde{\chi}_1^0$ or to $t\tilde{\chi}_1^0$. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, assuming unity branching fraction for the given decay.

Exclusion limits at 95% CL for top squark pair production, where the squarks decay to $c\tilde{\chi}_1^0$. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, assuming unity branching fraction for the given decay.

Exclusion limits at 95% CL for the mono-$\phi$ model, in which a resonantly-produced colored scalar decays to a massive Dirac fermion and a quark. Signal cross sections are calculated at leading order in $\alpha_S$, assuming unity branching fraction for the given decay.

Cross section limits for $\mathrm{LQ}\to\mathrm{q}\nu$, where $q=u,\,d,\,s,\,\mathrm{or}\,c$. Limits are at the 95% confidence level. Theory cross sections are LO for vector LQ, and NLO for scalar LQ. Branching ratio is assumed to be 100% to $\mathrm{q}\nu$.

Cross section limits for $\mathrm{LQ}\to\mathrm{b}\nu$. Limits are at the 95% confidence level. Theory cross sections are LO for vector LQ, and NLO for scalar LQ. Branching ratio is assumed to be 100% to $\mathrm{b}\nu$.

Cross section limits for $\mathrm{LQ}\to\mathrm{t}\nu$. Limits are at the 95% confidence level. Theory cross sections are LO for vector LQ, and NLO for scalar LQ. Branching ratios are assumed to be $\mathcal{B}(\mathrm{LQ}\to\mathrm{t}\nu)=1-\beta$, and $\mathcal{B}(\mathrm{LQ}\to\mathrm{b}\tau)=\beta$.

Predictions and observations for monojet signal regions

Predictions and observations for signal regions with $250 \leq H_\mathrm{T} < 450$ GeV

Predictions and observations for signal regions with $450 \leq H_\mathrm{T} < 575$ GeV and $N_\mathrm{j}<7$

Predictions and observations for signal regions with $450 \leq H_\mathrm{T} < 575$ GeV and $N_\mathrm{j}\geq7$

Predictions and observations for signal regions with $575 \leq H_\mathrm{T} < 1200$ GeV and $N_\mathrm{j}^\mathrm{hi}<4$

Predictions and observations for signal regions with $575 \leq H_\mathrm{T} < 1200$ GeV and $4\leq N_\mathrm{j}^\mathrm{hi}<7$

Predictions and observations for signal regions with $575 \leq H_\mathrm{T} < 1200$ GeV and $N_\mathrm{j}\geq7$

Predictions and observations for signal regions with $1200 \leq H_\mathrm{T} < 1500$ GeV and $N_\mathrm{j}^\mathrm{hi}<4$

Predictions and observations for signal regions with $1200 \leq H_\mathrm{T} < 1500$ GeV and $4\leq N_\mathrm{j}^\mathrm{hi}<7$

Predictions and observations for signal regions with $1200 \leq H_\mathrm{T} < 1500$ GeV and $N_\mathrm{j}\geq7$

Predictions and observations for signal regions with $H_\mathrm{T} \geq 1500$ GeV and $N_\mathrm{j}<7$

Predictions and observations for signal regions with $H_\mathrm{T} \geq 1500$ GeV and $N_\mathrm{j}\geq7$

Covariance matrix for the 282 signal regions of the inclusive $M_\mathrm{T2}$ search

Correlation matrix for the 282 signal regions of the inclusive $M_\mathrm{T2}$ search

Bin number definitions for the $M_\mathrm{T2}$ covariance and correlation matrices

Exclusion limits at 95% CL for direct gluino pair production, where the gluinos decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino is long-lived with $c\tau_0 = 10$ cm and mass O(100) MeV greater than the neutralino's mass. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$.

Exclusion limits at 95% CL for direct gluino pair production, where the gluinos decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino is long-lived with $c\tau_0 = 50$ cm and mass O(100) MeV greater than the neutralino's mass. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$.

Exclusion limits at 95% CL for direct gluino pair production, where the gluinos decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino is long-lived with $c\tau_0 = 200$ cm and mass O(100) MeV greater than the neutralino's mass. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$.

Exclusion limits at 95% CL for direct light squark pair production, where the squarks decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino is long-lived with $c\tau_0 = 10$ cm and mass O(100) MeV greater than the neutralino's mass. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, for a single light squark.

Exclusion limits at 95% CL for direct light squark pair production, where the squarks decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino is long-lived with $c\tau_0 = 50$ cm and mass O(100) MeV greater than the neutralino's mass. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, for a single light squark.

Exclusion limits at 95% CL for direct light squark pair production, where the squarks decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino is long-lived with $c\tau_0 = 200$ cm and mass O(100) MeV greater than the neutralino's mass. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, for a single light squark.

Exclusion limits at 95% CL for direct stop pair production, where the stops decay to either a top and the lightest neutralino, or a bottom and the lightest chargino, and the chargino is long-lived with $c\tau_0 = 10$ cm and mass O(100) MeV greater than the neutralino's mass. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$.

Exclusion limits at 95% CL for direct stop pair production, where the stops decay to either a top and the lightest neutralino, or a bottom and the lightest chargino, and the chargino is long-lived with $c\tau_0 = 50$ cm and mass O(100) MeV greater than the neutralino's mass. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$.

Exclusion limits at 95% CL for direct stop pair production, where the stops decay to either a top and the lightest neutralino, or a bottom and the lightest chargino, and the chargino is long-lived with $c\tau_0 = 200$ cm and mass O(100) MeV greater than the neutralino's mass. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$.

The maximum chargino mass excluded at 95% CL for direct gluino pair production, where the gluinos decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino mass is O(100) MeV greater than the neutralino's mass. The chargino's lifetime is varied from $c\tau_{0} = 1$ to 2000 cm while the gluino mass is fixed to 1900 GeV. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$. If all kinematically allowed chargino masses are excluded, the curves, including 68 and 95% expected, tend to overlap. At short decay lengths, horizontal exclusion lines are obtained from the inclusive analysis, as this is not affected by track reconstruction inefficiencies, which may arise when the chargino decays before the CMS tracker, and therefore shows better sensitivity to scenarios with very small lifetime compared to the disappearing track search, based on median expected limits.

The maximum chargino mass excluded at 95% CL for direct squark pair production, where the squarks decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino mass is O(100) MeV greater than the neutralino's mass. The chargino's lifetime is varied from $c\tau_{0} = 1$ to 2000 cm while the squark mass is fixed to 900 GeV. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, for a single light squark. If all kinematically allowed chargino masses are excluded, the curves, including 68 and 95% expected, tend to overlap. At short decay lengths, horizontal exclusion lines are obtained from the inclusive analysis, as this is not affected by track reconstruction inefficiencies, which may arise when the chargino decays before the CMS tracker, and therefore shows better sensitivity to scenarios with very small lifetime compared to the disappearing track search, based on median expected limits.

The maximum chargino mass excluded at 95% CL for direct squark pair production, where the squarks decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino mass is O(100) MeV greater than the neutralino's mass. The chargino's lifetime is varied from $c\tau_{0} = 1$ to 2000 cm while the squark mass is fixed to 1500 GeV. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, and the eight light squarks' masses are assumed to be degenerate. If all kinematically allowed chargino masses are excluded, the curves, including 68 and 95% expected, tend to overlap. At short decay lengths, horizontal exclusion lines are obtained from the inclusive analysis, as this is not affected by track reconstruction inefficiencies, which may arise when the chargino decays before the CMS tracker, and therefore shows better sensitivity to scenarios with very small lifetime compared to the disappearing track search, based on median expected limits.

Exclusion limits at 95% CL for direct stop pair production, where the stops decay to either a top and the lightest neutralino, or a bottom and the lightest chargino, and the chargino mass is O(100) MeV greater than the neutralino's mass. The chargino's lifetime is varied from $c\tau_{0} = 1$ to 2000 cm while the stop mass is fixed to 1000 GeV. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$. If all kinematically allowed chargino masses are excluded, the curves, including 68 and 95% expected, tend to overlap. At short decay lengths, horizontal exclusion lines are obtained from the inclusive analysis, as this is not affected by track reconstruction inefficiencies, which may arise when the chargino decays before the CMS tracker, and therefore shows better sensitivity to scenarios with very small lifetime compared to the disappearing track search, based on median expected limits.

Exclusion limits at 95% CL for direct gluino pair production, where the gluinos decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino mass is O(100) MeV greater than the neutralino's mass. The chargino's lifetime is varied from $c\tau_{0} = 5$ to 1000 cm while the gluino mass is fixed to 1600 GeV and the neutralino's mass is fixed to 1575 GeV. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$.

Exclusion limits at 95% CL for direct squark pair production, where the squarks decay to light-flavor quarks and either the lightest neutralino, or the lightest chargino, and the chargino mass is O(100) MeV greater than the neutralino's mass. The chargino's lifetime is varied from $c\tau_{0} = 5$ to 1000 cm while the squark mass is fixed to 2000 GeV and the neutralino's mass is fixed to 1000 GeV. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$, and the eight light squarks' masses are assumed to be degenerate.

Exclusion limits at 95% CL for direct stop pair production, where the stops decay to either a top and the lightest neutralino, or a bottom and the lightest chargino, and the chargino mass is O(100) MeV greater than the neutralino's mass. The chargino's lifetime is varied from $c\tau_{0} = 5$ to 1000 cm while the stop mass is fixed to 1100 GeV and the neutralino's mass is fixed to 1000 GeV. Signal cross sections are calculated at approximately NNLO+NNLL order in $\alpha_S$.

Predictions and observations for 2016 disappearing track signal regions

Predictions and observations for 2017-2018 pixel track signal regions

Predictions and observations for 2017-2018 medium (M) and long (L) length track signal regions

Covariance matrix for the 68 signal regions of the disappearing tracks $M_\mathrm{T2}$ search

Correlation matrix for the 68 signal regions of the disappearing tracks $M_\mathrm{T2}$ search