Expected and observed $95\%$ CL limits on the absolute value of the up-type quark coupling parameter $|\zeta_\mathrm{u}|$ for masses of the $A$ boson ranging from $20$ to $90\,\mathrm{GeV}$.

Observed $p_0$ values for the fits on the production cross-section for $gg\rightarrow A$ times the branching ratio for $A$ decaying into two $\tau$-leptons for $A$ boson masses from $20$ to $90\,\mathrm{GeV}$. For the fit on the up-type quark coupling parameter the $p_0$ values agree up to a precision of $O(10^{-5})$.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH3 at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 and ISR starting scale SpaceShower:pT0Ref = 1 GeV at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH3 and ISR starting scale SudakovCommon:pTmin=0.7 GeV at nucleon-nucleon center-of-mass energy from 38.8 GeV to 13 TeV.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-proton center-of-mass energy 38.8 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-proton center-of-mass energy 8000 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-nucleon center-of-mass energy 8160 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters BeamRemnants:PrimordialkThard in Pythia with the underlying-event tune CP5 at proton-nucleon center-of-mass energy 13000 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-proton center-of-mass energy 38.8 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-proton center-of-mass energy 8000 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-nucleon center-of-mass energy 8160 GeV versus the dilepton mass range of the process.

Tuned intrinsic kT parameters ShowerHandler:IntrinsicPtGaussian in Herwig with the underlying-event tune CH2 at proton-proton center-of-mass energy 13000 GeV versus the dilepton mass range of the process.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 38.8 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 62 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 200 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-anti-proton center-of-mass energy 1800 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-anti-proton center-of-mass energy 1960 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 2760 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 8000 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-nucleon center-of-mass energy 8160 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 13000 GeV when fitting to the CMS measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters among various generator settings at proton-proton center-of-mass energy 13000 GeV when fitting to the LHCb measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 38.8 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 62 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 200 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-anti-proton center-of-mass energy 1800 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-anti-proton center-of-mass energy 1960 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 2760 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 8000 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-nucleon center-of-mass energy 8160 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 13000 GeV when fitting to the CMS measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Pythia at proton-proton center-of-mass energy 13000 GeV when fitting to the LHCb measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 38.8 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 62 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 200 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-anti-proton center-of-mass energy 1800 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-anti-proton center-of-mass energy 1960 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 2760 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 8000 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-nucleon center-of-mass energy 8160 GeV.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 13000 GeV when fitting to the CMS measurement.

Covariance of the data uncertainty contribution to the tuned intrinsic kT parameters between the two generator settings of Herwig at proton-proton center-of-mass energy 13000 GeV when fitting to the LHCb measurement.

The model-independent 95\% \CL expected and observed upper limits set on ${\sigma(\PP\to 2\Pa+\PX)\mathcal{B}^2(\Pa\to 2\PGm)\alphaGen}$ over the range $0.21 < \MPa < 9\GeV$ for the combined 2016, 2017, and 2018 analyses. Mass ranges that overlap with \JPsi and \PgU resonances are excluded from the search

The 95\% \CL observed upper limits on the effective coupling $\CahLambda$ of the ALP to the SM Higgs assuming the branching fraction of the ALP to muons is 1 (blue) and 0.1 (orange), for both the expected (dashed) and observed (solid) limits

The 95\% \CL observed upper limits on the effective coupling $\cll/\Lambda$ of the ALP to the SM leptons. The orange shaded region represents the parameter space excluded by this search under three choices of $\CahLambda$ $1\TeV^{-2}$ (solid), $0.1\TeV^{-2}$ (dashed), and $0.01\TeV^{-2}$ (dotted)

The 95\% \CL observed upper limits on $\varepsilon^{2} \mathcal{B}(\ZDark \to \sDark \overline{\sDark}) \mathcal{B}^{2}(\sDark \rightarrow 2\PGm)$ for the vector portal model as a function of the dark scalar mass $\MsDark$ and dark vector boson mass $\MZDark$. The yellow and green bands indicate one and two standard deviation values of the limit, respectively. Because the model-independent limits are calculated only up to a dimuon mass of 60\GeV, the \sDark mass considered for these limits is below 60\GeV

The 95\% \CL observed upper limits for $\sigma(\PP\to\PhOneTwo\to 2\PaOne) \mathcal{B}^2(\PaOne\to 2\PGm)$ for the NMSSM as a function of $\MPaOne$ for $\MPhOneTwo$ = 90~GEV. The data presented here reflect the results of the 2017 and 2018 datasets combined with the previously published results of the 2016 dataset in \cite{CMS:2018jid}

The 95\% \CL observed upper limits for $\sigma(\PP\to\PhOneTwo\to 2\PaOne) \mathcal{B}^2(\PaOne\to 2\PGm)$ for the NMSSM as a function of $\MPaOne$ for $\MPhOneTwo$ = 125~GEV. The data presented here reflect the results of the 2017 and 2018 datasets combined with the previously published results of the 2016 dataset in \cite{CMS:2018jid}

The 95\% \CL observed upper limits for $\sigma(\PP\to\PhOneTwo\to 2\PaOne) \mathcal{B}^2(\PaOne\to 2\PGm)$ for the NMSSM as a function of $\MPaOne$ for $\MPhOneTwo$ = 150~GEV. The data presented here reflect the results of the 2017 and 2018 datasets combined with the previously published results of the 2016 dataset in \cite{CMS:2018jid}

The 95\% \CL observed upper limits (black solid curves) from this search as interpreted in the dark SUSY scenario for the process $\PP \to \Ph \to 2\nOne \to 2\gammaDark + 2\nDark \to 4\PGm + \PX$, with \mbox{$\MnOne = 60\GeV$} and $\MnDark = 1\GeV$. The limits are presented in the plane of $\varepsilon$ and $\MgammaDark$. The color gradient represents different branching fraction assumptions for \mbox{$\mathcal{B}(\Ph \to 2\nOne \to 2\gammaDark + 2\nDark)$}, ranging from dark orange (0.05\%) to light orange (10\%). The degradation of the limit around 1\GeV is attributed to the drop in the dimuon branching fraction $\mathcal{B}(\gammaDark \to 2\mu)$ due to the dimuon resonance of the $\phi$ meson~\cite{Batell:2009yf}

non prompt $\psi(2S)$ $\lambda_\theta$

Statistical covariance between bins of Table 2

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 3

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 4

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 5

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 6

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 7

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 8

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 9

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 10

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 11

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 12

Mean-$p_{T}$ of $K^{0}_{S}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 13

Mean-$p_{T}$ of $K^{0}_{S}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 14

Mean-$p_{T}$ of $K^{0}_{S}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 15

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 16

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 17

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 18

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 19

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 20

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 21

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 22

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 23

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 24

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 25

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 26

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 27

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 28

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 29

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 30

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 31

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 32

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 33

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the away region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 34

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the towards region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 35

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the transverse region vs. leading-jet $p_{T}$

Statistical covariance between bins of Table 36

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 37

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 38

Mean multiplicity of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 39

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 40

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 41

Mean scalar sum-$p_{T}$ of $K^{0}_{S}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 42

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 43

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 44

Ratio of the multiplicity of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 45

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 46

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 47

Ratio of the scalar sum-pt of $K^{0}_{S}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 48

Mean-$p_{T}$ of $K^{0}_{S}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 49

Mean-$p_{T}$ of $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 50

Mean-$p_{T}$ of $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 51

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 52

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 53

Mean multiplicity of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 54

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 55

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 56

Mean scalar sum-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ per unit $(\eta, \phi)$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 57

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 58

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 59

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 60

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 61

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 62

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to prompt charged particles in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 63

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 64

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 65

Ratio of the multiplicity of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 66

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 67

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 68

Ratio of the scalar sum-pt of $\Lambda$ and $\bar{\Lambda}$ to $K^{0}_{S}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 69

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the away region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 70

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the towards region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 71

Mean-$p_{T}$ of $\Lambda$ and $\bar{\Lambda}$ in the transverse region vs. $N_\textrm{ch,trans}$

Statistical covariance between bins of Table 72

Distribution of the diphoton invariant mass in validation region VR2. The solid histograms are stacked to show the SM expectations after the 2&times;2D background estimation technique is applied. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. Statistical and systematic uncertainties are indicated by the shaded area. The lower panel of each plot shows the ratio of the data to the SM prediction for the respective bin. The first and last bins include the underflows and overflows respectively.

Distribution of the missing transverse momentum in validation region VR2. The solid histograms are stacked to show the SM expectations after the 2&times;2D background estimation technique is applied. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. Statistical and systematic uncertainties are indicated by the shaded area. The lower panel of each plot shows the ratio of the data to the SM prediction for the respective bin. The first and last bins include the underflows and overflows respectively.

Distribution of the diphoton invariant mass with all selections of the signal regions applied, except on m_{&gamma;&gamma;} itself, for signal region SR1h. The background estimation techniques described in the text are applied. The different backgrounds are stacked to add up to the total SM prediction in each bin. The predicted yields for signal benchmark models of varying &chi;&#771;⁰₁ mass are also overlaid (not stacked) assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; ) to equal 100%. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The sizes of the statistical and systematic uncertainties are indicated by the shaded areas. The lower panels show the ratio of the data to the SM prediction. Arrows indicate the borders of the signal region (|m_{&gamma;&gamma;}-125 GeV|&lt;5 GeV). The first and last bins include the underflows and overflows respectively.

Distribution of the diphoton invariant mass with all selections of the signal regions applied, except on m_{&gamma;&gamma;} itself, for signal region SR1Z. The background estimation techniques described in the text are applied. The different backgrounds are stacked to add up to the total SM prediction in each bin. The predicted yields for signal benchmark models of varying &chi;&#771;⁰₁ mass are also overlaid (not stacked) assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; ) to equal 50%. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The sizes of the statistical and systematic uncertainties are indicated by the shaded areas. The lower panels show the ratio of the data to the SM prediction. Arrows indicate the borders of the signal region (|m_{&gamma;&gamma;}-125 GeV|&lt;5 GeV). The first and last bins include the underflows and overflows respectively.

Distribution of the diphoton invariant mass with all selections of the signal regions applied, except on m_{&gamma;&gamma;} itself, for signal region SR2. The background estimation techniques described in the text are applied. The different backgrounds are stacked to add up to the total SM prediction in each bin. The predicted yields for signal benchmark models of varying &chi;&#771;⁰₁ mass are also overlaid (not stacked) assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; ) to equal 100%. Background and signal predictions are normalised to the luminosity. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The sizes of the statistical and systematic uncertainties are indicated by the shaded areas. The lower panels show the ratio of the data to the SM prediction. Arrows indicate the borders of the signal region (|m_{&gamma;&gamma;}-125 GeV|&lt;5 GeV). The first and last bins include the underflows and overflows respectively.

Observed and expected limits on the pure higgsino cross-section at 95% CL assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; )=100% for different &chi;&#771;⁰₁ masses, obtained by a statistical combination of the three signal regions SR1h, SR1Z and SR2. The inner and outer bands indicate the 1&sigma; and 2&sigma; variation on the expected limit respectively.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Observed and expected 95% CL limits on the pure-higgsino branching fraction to B(&chi;&#771;⁰₁ &rarr; hG&#771; ) as a function of the higgsino mass m(&chi;&#771;⁰₁) assuming it decays via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;. Limits are obtained by performing a statistical combination of the three signal regions SR1h, SR1Z and SR2. The &plusmn; 1&sigma; variation on the expected limit is shown. The dotted lines indicate the observed limit obtained by a variation of theoretical prediction for the neutralino production cross-section by &plusmn;1 &sigma;. Values of B(&chi;&#771;⁰₁ &rarr; hG&#771; ) larger than the observed 95% CL limit are excluded, as indicated by the hatched area.

Numbers of signal and background events in the signal regions. The respective background estimation techniques are applied. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The different backgrounds are stacked to add up to the total Standard Model prediction in each bin. The predicted yields for signal benchmark models of varying &chi;&#771;⁰₁ mass are also plotted (not stacked), assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; )=100% and a &chi;&#771;⁰₁ mass of 130 or 200 GeV. The statistical and systematic uncertainties are indicated by the shaded areas in the top plot. The bottom panel shows the statistical significance&nbsp;<a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2020-025/">[Ref]</a> of the difference between the SM prediction and the observed data in each region.

Observed 95% CL limits in pb on the pure higgsino cross-section, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Limits are obtained by a statistical combination of the three signal regions SR1h, SR1Z and SR2, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Acceptances for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Acceptances are provided separately for signal region SR1h, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Acceptances for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Acceptances are provided separately for signal region SR1Z, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Acceptances for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Acceptances are provided separately for signal region SR2, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Efficiencies for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Efficiencies are provided separately for signal region SR1h, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Efficiencies for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Efficiencies are provided separately for signal region SR1Z, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Efficiencies for all signal model points considered in the analysis, shown in the m(&chi;&#771;⁰₁)-B(&chi;&#771;⁰₁ &rarr; hG&#771; ) plane. Efficiencies are provided separately for signal region SR2, assuming the neutralino to decay via either &chi;&#771;⁰₁&rarr; hG&#771; or &chi;&#771;⁰₁&rarr; ZG&#771;.

Observed and expected numbers of events in the three signal regions. The background category "h (other)" includes events originating from VBF, Vh, ggF, thq, thW and bb&#772;h, all subdominant in this signature. The table also includes model-independent 95% CL upper limits on the visible number of BSM events (S⁹⁵_obs), the number of BSM events given the expected number of background events (S⁹⁵_exp) and the visible BSM cross-section (&lang;&epsilon; &sigma;&rang;_obs⁹⁵), all calculated from pseudo-experiments. The discovery p-value (p₀) is also shown and its value is capped at 0.5 if the observed number of events is below the expected number of events.

Cutflows of two benchmark signal points assuming B(&chi;&#771;⁰₁ &rarr; hG&#771; )=100% for all three discovery signal regions. The initial selection includes the leptons veto. Only statistical uncertainties are included. Expected yields are normalised to a luminosity of 139 fb^-1.

Observed exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$ and a $-1 \sigma$ deviation of the NNLO+NNLL theoretical cross-section of a $\tilde{t}_1$ pair-production.

Expected exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

The $1\sigma$ variation of expected 95% CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

The $2\sigma$ variation of expected 95%CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Observed exclusion limits at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Observed exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{ GeV}$ and a $+1 \sigma$ deviation of the NNLO+NNLL theoretical cross-section of a $\tilde{t}_1$ pair-production.

Observed exclusion limits at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{ GeV}$ and a $-1 \sigma$ deviation of the NNLO+NNLL theoretical cross-section of a $\tilde{t}_1$ pair-production.

Expected exclusion limits at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

The $1\sigma$ variation of expected 95% CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

The $2\sigma$ variation of expected 95%CL exclusion contour obtained by varying MC statistical uncertainties, detector-related systematic uncertainties, and theoretical uncertainties in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Observed upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$

Expected upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$

Observed upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Expected upper limits on the top-spartner pair production cross-section in fb at the 95% CL in the $m_{\tilde{t}_1} - $BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1)$ plane, assuming $m_{\tilde\chi^0_1} = 1 \mathrm{GeV}$.

Post-fit distribution of $N_{\mathrm{tops}}^{\mathrm{DNN}} (\mathrm{R=1.0})$ in the SRA signal region presented without the associated SRA applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{T2}}(j^{b}_{R=1.0}, c)$ in the SRA signal region presented without the associated SRA applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $p_{\mathrm{T}}(c_{1})$ in the SRB signal region presented without the associated SRB applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{T}}(j, \mathrm{E}_{\mathrm{T}}^{\mathrm{miss}})_{\mathrm{close}}$ in the SRB signal region presented without the associated SRB applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $\mathrm{E}_{\mathrm{T}}^{\mathrm{miss}} \textrm{Significance}$ in the SRC signal region presented without the associated SRC applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{T}}(j, \mathrm{E}_{\mathrm{T}}^{\mathrm{miss}})_{\mathrm{close}}$ in the SRC signal region presented without the associated SRC applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of NN signal score in the SRD signal region presented without the associated SRD applied to the variable. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Post-fit distribution of $m_{\mathrm{eff}}$ in the SRD signal region. For each bin yields for the data and total SM prediction are provided. The SM prediction is provided with the total uncertainty, including the MC statistical uncertainty, detector-related systematic uncertainties and theoretical uncertainties. The rightmost bin includes overflow events.

Pull plots showing the SRA, SRB and SRC post-fit data and SM agreement using the background-only fit configuration

Pull plots showing the SRD post-fit data and SM agreement using the background-only fit configuration

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region A. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region B. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region C. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 750. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1000. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1250. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1500. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 1750. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Cutflow for the reference point $(m_{\tilde{t}_1}, m_{\tilde\chi^0_1})=$ (900,1) , (700,300), (550,375) in the Signal region D 2000. Results are shown including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$.

Signal acceptance in the SRA$^{[450,575]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRA$^{[450,575]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRA$^{\geq 575}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRA$^{\geq 575}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRB$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRB$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRB$^{[150,400]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRB$^{[150,400]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRB$^{\geq 400}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRB$^{\geq 400}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{[100,150]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{[150,300]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{[150,300]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{[300,500]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{[300,500]}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRC$^{\geq 500}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRC$^{\geq 500}$ region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1250 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1250 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1500 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1500 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD1750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD1750 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Signal acceptance in the SRD2000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$. Please mind that the acceptance given in the table is multiplied by factor of $10^{3}$ (a factor of 10 larger than in the plots).

Signal efficiency in the SRD2000 region in the $\tilde{t}_1 - \tilde\chi^0_1$ mass plane, assuming BR$(\tilde{t}_1 \rightarrow t + \tilde\chi^0_1) = 0.5$.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected (dashed black line) and observed (solid red line) 95% CL exclusion limits on the higgsino simplified model being considered. These are shown with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively. The limits set by the latest ATLAS searches using the soft lepton and disappearing track signatures are illustrated by the blue and green regions, respectively, while the limit imposed by the LEP experiments is shown in gray. The dot-dashed gray line indicates the predicted mass-splitting for the pure higgsino scenario.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed CLs values per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Expected and observed cross-section upper-limit per signal point represented by the grey numbers. The expected (dashed) and observed (solid) 95% CL exclusion limits are overlaid along with $\pm 1\sigma_{\mathrm{exp}}$ (yellow band) from experimental systematic and statistical uncertainties, and with $\pm 1\sigma_{\mathrm{theory}}^{\mathrm{SUSY}}$ (red dotted lines) from signal cross-section uncertainties, respectively.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Truth-level signal acceptances for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$) in a SR with the $S(d_0)$ requirement removed. The acceptance is defined as the fraction of accepted events divided by the total number of events in the generator-level signal Monte Carlo simulation, where the signal candidate track is identified as the charged particle with the largest distance between the interaction vertex and the secondary vertex of the higgsino decays.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-Low for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Signal efficiencies in SR-High for each production process ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$), defined by the number of events of reconstructed-level signal simulation divided by the number of events obtained at generator level, where the $S(d_0)$ selecton efficiency has the largest impact. The higgsino decay products from $\Delta \mathrm{m}(\tilde{\chi}_1^\pm,\tilde{\chi}_1^0) < 0.4$ GeV signal have $p_{\mathrm{T}}$ too low to be reconstructed as the signal candidate tracks, and therefore the identified signal candidate tracks are typically from pile-up collisions or underlying events similar to the QCD track background, causing a low $S(d_0)$ selection efficiency in these plots.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 1.5, 1.0, and 0.75 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 1.5, 1.0, and 0.75 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 0.5, 0.35, and 0.25 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.

Event selection cutflows for signal samples with $m(\tilde{\chi}_{1}^0)$ = 150 GeV and $\Delta m(\tilde{\chi}_{1}^\pm, \tilde{\chi}_{1}^0)$ = 0.5, 0.35, and 0.25 GeV, including all six production processes ($\tilde{\chi}_1^\pm \tilde{\chi}_1^0$, $\tilde{\chi}_1^\pm \tilde{\chi}_2^0$, $\tilde{\chi}_1^+ \tilde{\chi}_1^-$, and $\tilde{\chi}_2^0 \tilde{\chi}_1^0$). The cross-section used to obtain the initial number of events ($\sigma(\mathrm{n}_{\mathrm{jets}}) \geq 1$) refers to an emission of at least one gluon or quark with $p_{\mathrm{T}} > 50$ GeV at the parton level.