$D^+ D^0$~mass distributions for selected candidates with the $D^0$ background subtracted. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Mass distributions for selected $D^+ D^+$ candidates with the $D^0$ background subtracted. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Mass distributions for selected $D^+ D^0 \pi^+$ candidates with the $D^0$ background subtracted. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Background-subtracted distributions for the multiplicity of tracks reconstructed in the vertex detector for $T_{cc}^+\to D^0 D^0 \pi^+$ signal, low-mass $D^0\bar{D}^0$ and $D^0D^0$ pairs. The binning scheme is chosen to have an approximately uniform distribution for $D^0\bar{D}^0$ pairs. The distributions for the $D^0\bar{D}^0$ and $D^0D^0$ pairs are normalised to the same yields as the $T_{cc}^+\to D^0 D^0 \pi^+$ signal. For better visualisation, the points are slightly displaced from the bin centres. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Background-subtracted transverse momentum spectra for $T_{cc}^+\to D^0 D^0 \pi^+$ signal, low-mass $D^0\bar{D}^0$ and $D^0D^0$ pairs. The binning scheme is chosen to have an approximately uniform distribution for $D^0\bar{D}^0$ pairs. The distributions for the $D^0\bar{D}^0$ and $D^0D^0$ pairs are normalised to the same yields as the $T_{cc}^+\to D^0 D^0 \pi^+$ signal. For better visualisation, the points are slightly displaced from the bin centres. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Mass distributions for selected $D^0 \bar{D}^0$ candidates with the $D^0$ background subtracted. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Mass distributions for selected $D^0 D^0$ candidates with the $D^0$ background subtracted. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Mass distributions for selected $\bar{D}^0D^0\pi^+$ candidates with the $D^0$ background subtracted. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Mass distributions for selected $D^0D^0\pi^+$ candidates with the $D^0$ background subtracted. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Mass distributions for selected $D^0D^-$ candidates with the $D^0$ background subtracted. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Mass distributions for selected $D^0D^+$ candidates with the $D^0$ background subtracted. Uncertainties on the data points are statistical only and represent one standard deviation, calculated as a sum in quadrature of the assigned weights from the background-subtraction procedure.

Distribution of $D^0 D^0 \pi^+$ mass where the contribution of the non-$D^0$ background has been statistically subtracted by assigning the a weight to every candidate.

Mass distribution for $D^0 \pi^+$ pairs from selected $D^0 D^0 \pi^+$ candidates with a mass below the $D^{*+}D^0$ mass threshold with non-$D^0$ background subtracted by assigning the a weight to every candidate.

$D^0 D^0$~mass distributions for selected candidates with the $D^0$ background subtracted by assigning the a weight to every candidate.

$D^+ D^0$~mass distributions for selected candidates with the $D^0$ background subtracted by assigning the a weight to every candidate.

Mass distributions for selected $D^+ D^+$ candidates with the $D^0$ background subtracted by assigning the a weight to every candidate.

Mass distributions for selected $D^+ D^0 \pi^+$ candidates with the $D^0$ background subtracted by assigning the a weight to every candidate.

Statistical correlation matrix for the one-dimensional $y^{Z}$ measurement.

Statistical correlation matrix for the one-dimensional $p_{T}^{Z}$ measurement.

Statistical correlation matrix for the one-dimensional $\phi_{\eta}^{*}$ measurement.

Correlation matrix for the efficiency uncertainty of the one-dimensional $y^{Z}$ measurement.

Correlation matrix for the efficiency uncertainty of the one-dimensional $p_{T}^{Z}$ measurement.

Correlation matrix for the efficiency uncertainty of the one-dimensional $\phi_{\eta}^{*}$ measurement.

Correlation matrix for the individual sources of systematic uncertainty considered in the analysis, for the combined opposite sign (OS) and same sign (SS) binned-template profile likelihood fit to data. The OS or SS refers to the charge signs of the primary lepton and the soft muon. The gamma parameters are NPs used to describe the effect of the limited statistics of the samples.

The Bose-Einstein correlation (BEC) parameter R as a function of n_ch for HMT events using different MC generators in the calculation of R₂(Q). The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.

The Bose-Einstein correlation (BEC) parameter R as a function of k_T for MB events using different MC generators in the calculation of R₂(Q). The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.

The Bose-Einstein correlation (BEC) parameter λ as a function of k_T for MB events using different MC generators in the calculation of R₂(Q). The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.

The two-particle double-ratio correlation function, R₂(Q), for pp collisions for track p_T >100 MeV at √s=13 TeV in the multiplicity interval 71 ≤ n_ch < 80 for the minimum-bias (MB) events. The blue dashed and red solid lines show the results of the exponential and Gaussian fits, respectively. The region excluded from the fits is shown. The statistical uncertainty and the systematic uncertainty for imperfections in the data reconstruction procedure are added in quadrature.

The two-particle double-ratio correlation function, R₂(Q), for pp collisions for track p_T >100 MeV at √s=13 TeV in the multiplicity interval 231 ≤ n_ch < 300 for the high-multiplicity track (HMT) events. The blue dashed and red solid lines show the results of the exponential and Gaussian fits, respectively. The region excluded from the fits is shown. The statistical uncertainty and the systematic uncertainty for imperfections in the data reconstruction procedure are added in quadrature.

The dependence of the correlation strength, λ(m_ch), on rescaled multiplicity, m_ch, obtained from the exponential fit of the R₂(Q) correlation functions for tracks with p_T > 100  MeV and p_T > 500 MeV at √s = 13  TeV for the minimum-bias (MB) and high multiplicity track (HMT) data. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the exponential fit of λ(m_ch) for p_T >100 MeV and p_T >500 MeV, respectively.

The dependence of the correlation strength, λ(m_ch), on rescaled multiplicity, m_ch, obtained from the exponential fit of the R₂(Q) correlation functions for tracks with p_T > 100  MeV and p_T > 500 MeV at √s = 13  TeV for the minimum-bias (MB) and high multiplicity track (HMT) data. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the exponential fit of λ(m_ch) for p_T >100 MeV and p_T >500 MeV, respectively.

The dependence of the correlation strength, λ(m_ch), on rescaled multiplicity, m_ch, obtained from the exponential fit of the R₂(Q) correlation functions for tracks with p_T > 100  MeV and p_T > 500 MeV at √s = 13  TeV for the minimum-bias (MB) and high multiplicity track (HMT) data. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the exponential fit of λ(m_ch) for p_T >100 MeV and p_T >500 MeV, respectively.

The dependence of the correlation strength, λ(m_ch), on rescaled multiplicity, m_ch, obtained from the exponential fit of the R₂(Q) correlation functions for tracks with p_T > 100  MeV and p_T > 500 MeV at √s = 13  TeV for the minimum-bias (MB) and high multiplicity track (HMT) data. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the exponential fit of λ(m_ch) for p_T >100 MeV and p_T >500 MeV, respectively.

The dependence of the source radius, R(m_ch), on m_ch. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m_ch) for ∛m_ch < 1.2 for p_T >100 MeV and p_T >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m_ch > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m_ch) for ∛m_ch > 1.45 with p_T >100 MeV and for ∛m_ch > 1.6 with p_T >500 MeV, respectively.

The dependence of the source radius, R(m_ch), on m_ch. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m_ch) for ∛m_ch < 1.2 for p_T >100 MeV and p_T >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m_ch > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m_ch) for ∛m_ch > 1.45 with p_T >100 MeV and for ∛m_ch > 1.6 with p_T >500 MeV, respectively.

The dependence of the source radius, R(m_ch), on m_ch. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m_ch) for ∛m_ch < 1.2 for p_T >100 MeV and p_T >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m_ch > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m_ch) for ∛m_ch > 1.45 with p_T >100 MeV and for ∛m_ch > 1.6 with p_T >500 MeV, respectively.

The dependence of the source radius, R(m_ch), on m_ch. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m_ch) for ∛m_ch < 1.2 for p_T >100 MeV and p_T >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m_ch > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m_ch) for ∛m_ch > 1.45 with p_T >100 MeV and for ∛m_ch > 1.6 with p_T >500 MeV, respectively.

The dependence of the R(m_ch) on ∛m_ch. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m_ch) for ∛m_ch < 1.2 for p_T >100 MeV and p_T >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m_ch > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m_ch) for ∛m_ch > 1.45 with p_T >100 MeV and for ∛m_ch > 1.6 with p_T >500 MeV, respectively

The dependence of the R(m_ch) on ∛m_ch. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m_ch) for ∛m_ch < 1.2 for p_T >100 MeV and p_T >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m_ch > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m_ch) for ∛m_ch > 1.45 with p_T >100 MeV and for ∛m_ch > 1.6 with p_T >500 MeV, respectively

The dependence of the R(m_ch) on ∛m_ch. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m_ch) for ∛m_ch < 1.2 for p_T >100 MeV and p_T >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m_ch > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m_ch) for ∛m_ch > 1.45 with p_T >100 MeV and for ∛m_ch > 1.6 with p_T >500 MeV, respectively

The dependence of the R(m_ch) on ∛m_ch. The uncertainties represent the sum in quadrature of the statistical and asymmetric systematic contributions. The black and blue solid curves represent the fit of R(m_ch) for ∛m_ch < 1.2 for p_T >100 MeV and p_T >500 MeV, respectively. The black and blue dotted curves are extensions of the black and blue solid curves beyond ∛m_ch > 1.2, respectively. The black and brown dashed curves represent the saturation value of R(m_ch) for ∛m_ch > 1.45 with p_T >100 MeV and for ∛m_ch > 1.6 with p_T >500 MeV, respectively

Comparison of single-ratio two-particle correlation functions, using the unlike-charge particle (UCP) pair reference sample, for minimum-bias (MB) events, showing C₂^data(Q) (top panel) at 13 TeV (black circles) and 7 TeV (open blue circles), and the ratio of C₂^7 TeV (Q) to C₂^13 TeV (Q) (bottom panel). Comparison of C₂^data (Q) for representative multiplicity region 3.09 < m_ch ≤ 3.86. The statistical and systematic uncertainties, combined in quadrature, are presented. The systematic uncertainties include track efficiency, Coulomb correction, non-closure and multiplicity-unfolding uncertainties.

Comparison of single-ratio two-particle correlation functions, using the unlike-charge particle (UCP) pair reference sample, for minimum-bias (MB) events, showing C₂^data(Q) (top panel) at 13  TeV (black circles) and 7  TeV (open blue circles), and the ratio of C₂^7 TeV (Q) to C₂^13 TeV (Q) (bottom panel). Comparison of C₂^data (Q) for representative k_T region 400 < k_T ≤500  MeV. The statistical and systematic uncertainties, combined in quadrature, are presented. The systematic uncertainties include track efficiency, Coulomb correction, non-closure and multiplicity-unfolding uncertainties.

The k_T dependence of the correlation strength, λ(k_T), obtained from the exponential fit to the R₂(Q) correlation functions for events with multiplicity n_ch ≥ 2 and transfer momentum of tracks with p_T >100 MeV and p_T >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to λ(k_T).

The k_T dependence of the correlation strength, λ(k_T), obtained from the exponential fit to the R₂(Q) correlation functions for events with multiplicity n_ch ≥ 2 and transfer momentum of tracks with p_T >100 MeV and p_T >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to λ(k_T).

The k_T dependence of the correlation strength, λ(k_T), obtained from the exponential fit to the R₂(Q) correlation functions for events with multiplicity n_ch ≥ 2 and transfer momentum of tracks with p_T >100 MeV and p_T >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to λ(k_T).

The k_T dependence of the correlation strength, λ(k_T), obtained from the exponential fit to the R₂(Q) correlation functions for events with multiplicity n_ch ≥ 2 and transfer momentum of tracks with p_T >100 MeV and p_T >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to λ(k_T).

The k_T dependence of the source radius, R(k_T), obtained from the exponential fit to the R₂(Q) correlation functions for events with multiplicity n_ch ≥ 2 and transfer momentum of tracks with p_T >100 MeV and p_T >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to R(k_T).

The k_T dependence of the source radius, R(k_T), obtained from the exponential fit to the R₂(Q) correlation functions for events with multiplicity n_ch ≥ 2 and transfer momentum of tracks with p_T >100 MeV and p_T >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to R(k_T).

The k_T dependence of the source radius, R(k_T), obtained from the exponential fit to the R₂(Q) correlation functions for events with multiplicity n_ch ≥ 2 and transfer momentum of tracks with p_T >100 MeV and p_T >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to R(k_T).

The k_T dependence of the source radius, R(k_T), obtained from the exponential fit to the R₂(Q) correlation functions for events with multiplicity n_ch ≥ 2 and transfer momentum of tracks with p_T >100 MeV and p_T >500 MeV at √s=13 TeV for the minimum-bias (MB) and high-multiplicity track (HMT) events. The uncertainties represent the sum in quadrature of the statistical and systematic contributions. The curves represent the exponential fits to R(k_T).

The two-dimensional dependence on m_ch and k_T for p_T > 100 MeV for the correlation strength, λ, obtained from the exponential fit to the R₂(Q) correlation functions using the MB sample for m_ch ≤ 3.08 and the HMT sample for m_ch > 3.08.

The two-dimensional dependence on m_ch and k_T for p_T > 100 MeV for the source radius, R, obtained from the exponential fit to the R₂(Q) correlation functions using the MB sample for m_ch ≤ 3.08 and the HMT sample for m_ch > 3.08.

The parameter λ for p_T > 100 MeV as a function of k_T in selected low m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 100 MeV as a function of k_T in selected low m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 100 MeV as a function of k_T in selected high m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 100 MeV as a function of k_T in selected high m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 100 MeV as a function of m_ch in k_T intervals between 0.1 and 0.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 100 MeV as a function of m_ch in k_T intervals between 0.1 and 0.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 100 MeV as a function of m_ch in k_T intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 100 MeV as a function of m_ch in k_T intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 100 MeV as a function of k_T in selected low m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 100 MeV as a function of k_T in selected low m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 100 MeV as a function of k_T in selected high m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 100 MeV as a function of k_T in selected high m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 100 MeV as a function of m_ch in k_T intervals between 0.1 and 0.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 100 MeV as a function of m_ch in k_T intervals between 0.1 and 0.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 100 MeV as a function of m_ch in k_T intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 100 MeV as a function of m_ch in k_T intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The fit parameter μ describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter μ on m_ch for tracks with p_T >100 MeV (p_T >500 MeV).

The fit parameter μ describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter μ on m_ch for tracks with p_T >100 MeV (p_T >500 MeV).

The fit parameter μ describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter μ on m_ch for tracks with p_T >100 MeV (p_T >500 MeV).

The fit parameter μ describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter μ on m_ch for tracks with p_T >100 MeV (p_T >500 MeV).

The fit parameter ν describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter ν on m_ch for tracks with p_T >100 MeV (p_T >500 MeV).

The fit parameter ν describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter ν on m_ch for tracks with p_T >100 MeV (p_T >500 MeV).

The fit parameter ν describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter ν on m_ch for tracks with p_T >100 MeV (p_T >500 MeV).

The fit parameter ν describing the dependence of the correlation strength, λ, on charged-particle scaled multiplicity, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid (blue dashed) curve represents the exponential fit of the dependence of parameter ν on m_ch for tracks with p_T >100 MeV (p_T >500 MeV).

The parameter ξ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m_ch, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the saturated value of the parameter ξ for m_ch > 3.0 for tracks with p_T >100 MeV and for m_ch > 2.8 for tracks with p_T >500 MeV, respectively.

The parameter ξ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m_ch, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the saturated value of the parameter ξ for m_ch > 3.0 for tracks with p_T >100 MeV and for m_ch > 2.8 for tracks with p_T >500 MeV, respectively.

The parameter ξ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m_ch, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the saturated value of the parameter ξ for m_ch > 3.0 for tracks with p_T >100 MeV and for m_ch > 2.8 for tracks with p_T >500 MeV, respectively.

The parameter ξ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m_ch, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the saturated value of the parameter ξ for m_ch > 3.0 for tracks with p_T >100 MeV and for m_ch > 2.8 for tracks with p_T >500 MeV, respectively.

The parameter κ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m_ch, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the exponential fit to the parameter κ for tracks with p_T >100 MeV and for tracks with p_T >500 MeV, respectively.

The parameter κ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m_ch, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the exponential fit to the parameter κ for tracks with p_T >100 MeV and for tracks with p_T >500 MeV, respectively.

The parameter κ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m_ch, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the exponential fit to the parameter κ for tracks with p_T >100 MeV and for tracks with p_T >500 MeV, respectively.

The parameter κ describing the dependence of the source radius, R, on charged-particle scaled multiplicity, m_ch, for track p_T>100 MeV and track p_T>500 MeV in the minimum-bias (MB) and high-multiplicity track (HMT) samples at √s = 13 TeV. The error bars and boxes represent the statistical and systematic contributions, respectively. The black solid and blue dashed curves represent the exponential fit to the parameter κ for tracks with p_T >100 MeV and for tracks with p_T >500 MeV, respectively.

The two-dimensional dependence on m_ch and k_T for p_T > 500 MeV for the correlation strength, λ, obtained from the exponential fit to the R₂(Q) correlation functions using the MB sample for m_ch ≤ 3.08 and the HMT sample for m_ch > 3.08.

The two-dimensional dependence on m_ch and k_T for p_T > 500 MeV for the source radius, R, obtained from the exponential fit to the R₂(Q) correlation functions using the MB sample for m_ch ≤ 3.08 and the HMT sample for m_ch > 3.08.

The parameter λ for p_T > 500 MeV as a function of k_T in selected low m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 500 MeV as a function of k_T in selected low m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 500 MeV as a function of k_T in selected high m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 500 MeV as a function of k_T in selected high m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 500 MeV as a function of m_ch in k_T intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter λ for p_T > 500 MeV as a function of m_ch in k_T intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 500 MeV as a function of k_T in selected low m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 500 MeV as a function of k_T in selected low m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 500 MeV as a function of k_T in selected high m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 500 MeV as a function of k_T in selected high m_ch intervals. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 500 MeV as a function of m_ch in k_T intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

The parameter R for p_T > 500 MeV as a function of m_ch in k_T intervals between 0.5 and 1.5 GeV. The error bars and boxes represent the statistical and systematic contributions, respectively.

ATLAS and CMS results for the source radius R as a function of n_ch in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p_T > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n_ch at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.

ATLAS and CMS results for the source radius R as a function of n_ch in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p_T > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n_ch at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.

ATLAS and CMS results for the source radius R as a function of n_ch in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p_T > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n_ch at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.

ATLAS and CMS results for the source radius R as a function of ∛n_ch in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p_T > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n_ch at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.

ATLAS and CMS results for the source radius R as a function of ∛n_ch in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p_T > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n_ch at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.

ATLAS and CMS results for the source radius R as a function of ∛n_ch in pp interactions at 13 TeV. The CMS results (open circles) have been adjusted (by the CMS collaboration) to the ATLAS kinematic region∶ p_T > 100 MeV and |η|<2.5. The ATLAS uncertainties are the sum in quadrature of the statistical and asymmetric systematic uncertainties. For CMS, only the systematic uncertainties are shown since the statistical uncertainties are smaller than the marker size. The dashed blue (ATLAS) and black (CMS) lines represent the fit to ∛n_ch at low multiplicity, continued as dotted lines beyond the fit range. The solid green (ATLAS) and broken black (CMS) lines indicate the plateau level at high multiplicity.

Systematic uncertainties (in percent) in the correlation strength, λ, and source radius, R, for the exponential fit of the two-particle double-ratio correlation functions, R₂(Q), for p_T > 100 MeV at √s= 13 TeV for the MB and HMT events. The choice of MC generator gives rise to asymmetric uncertainties, denoted by uparrow and downarrow. This asymmetry propagates through to the cumulative uncertainty. The columns under ‘Uncertainty range’ show the range of systematic uncertainty from the fits in the various n_ch intervals.

The results of the fits to the dependencies of the correlation strength, λ, and source radius, R, on the average rescaled charged-particle multiplicity, m_ch, for |η| < 2.5 and both p_T > 100 MeV and p_T > 500 MeV at √s = 13 TeV for the minimum-bias (MB) and the high-multiplicity track (HMT) events. The parameters γ and δ resulting from a joint fit to the MB and HMT data are presented. The total uncertainties are shown.

The results of the fits to the dependencies of the correlation strength, λ, and source radius, R, on the pair average transverse momentum, k_T, for various functional forms and for minimum-bias (MB) and high-multiplicity track (HMT) events for p_T > 100 MeV and p_T > 500 MeV at √s = 13 TeV. The total uncertainties are shown.

The Bose-Einstein correlation (BEC) parameters λ and R as a function of n_ch and k_T using different MC generators in the calculation of R₂(Q). (a) λ versus n_ch for MB events, (b) λ versus n_ch for HMT events, (c) λ versus k_T and (d) R versus k_T for MB events. The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.

The Bose-Einstein correlation (BEC) parameters λ and R as a function of n_ch and k_T using different MC generators in the calculation of R₂(Q). (a) λ versus n_ch for MB events, (b) λ versus n_ch for HMT events, (c) λ versus k_T and (d) R versus k_T for MB events. The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.

The Bose-Einstein correlation (BEC) parameters λ and R as a function of n_ch and k_T using different MC generators in the calculation of R₂(Q). (a) λ versus n_ch for MB events, (b) λ versus n_ch for HMT events, (c) λ versus k_T and (d) R versus k_T for MB events. The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.

The Bose-Einstein correlation (BEC) parameters λ and R as a function of n_ch and k_T using different MC generators in the calculation of R₂(Q). (a) λ versus n_ch for MB events, (b) λ versus n_ch for HMT events, (c) λ versus k_T and (d) R versus k_T for MB events. The uncertainties shown are statistical. The lower panel of each plot shows the ratio of the BEC parameters obtained using EPOS LHC (red circles), Pythia 8 Monash (blue squares) and Herwig++ UE-EE-5 (green triangles) compared with the parameters obtained using Pythia 8 A2. The gray band in the lower panels is the MC systematic uncertainty, obtained as explained in the text.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 10, (b) 11 < n_ch ≤ 20, (c) 21 < n_ch ≤ 30, (d) 31 < n_ch ≤ 40, (e) 41 < n_ch ≤ 50, (f) 51 < n_ch ≤ 60, (g) 61 < n_ch ≤ 70, (h) 71 < n_ch ≤ 80 and (i) 81 < n_ch ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 10, (b) 11 < n_ch ≤ 20, (c) 21 < n_ch ≤ 30, (d) 31 < n_ch ≤ 40, (e) 41 < n_ch ≤ 50, (f) 51 < n_ch ≤ 60, (g) 61 < n_ch ≤ 70, (h) 71 < n_ch ≤ 80 and (i) 81 < n_ch ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 10, (b) 11 < n_ch ≤ 20, (c) 21 < n_ch ≤ 30, (d) 31 < n_ch ≤ 40, (e) 41 < n_ch ≤ 50, (f) 51 < n_ch ≤ 60, (g) 61 < n_ch ≤ 70, (h) 71 < n_ch ≤ 80 and (i) 81 < n_ch ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 10, (b) 11 < n_ch ≤ 20, (c) 21 < n_ch ≤ 30, (d) 31 < n_ch ≤ 40, (e) 41 < n_ch ≤ 50, (f) 51 < n_ch ≤ 60, (g) 61 < n_ch ≤ 70, (h) 71 < n_ch ≤ 80 and (i) 81 < n_ch ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 10, (b) 11 < n_ch ≤ 20, (c) 21 < n_ch ≤ 30, (d) 31 < n_ch ≤ 40, (e) 41 < n_ch ≤ 50, (f) 51 < n_ch ≤ 60, (g) 61 < n_ch ≤ 70, (h) 71 < n_ch ≤ 80 and (i) 81 < n_ch ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 10, (b) 11 < n_ch ≤ 20, (c) 21 < n_ch ≤ 30, (d) 31 < n_ch ≤ 40, (e) 41 < n_ch ≤ 50, (f) 51 < n_ch ≤ 60, (g) 61 < n_ch ≤ 70, (h) 71 < n_ch ≤ 80 and (i) 81 < n_ch ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 10, (b) 11 < n_ch ≤ 20, (c) 21 < n_ch ≤ 30, (d) 31 < n_ch ≤ 40, (e) 41 < n_ch ≤ 50, (f) 51 < n_ch ≤ 60, (g) 61 < n_ch ≤ 70, (h) 71 < n_ch ≤ 80 and (i) 81 < n_ch ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 10, (b) 11 < n_ch ≤ 20, (c) 21 < n_ch ≤ 30, (d) 31 < n_ch ≤ 40, (e) 41 < n_ch ≤ 50, (f) 51 < n_ch ≤ 60, (g) 61 < n_ch ≤ 70, (h) 71 < n_ch ≤ 80 and (i) 81 < n_ch ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 10, (b) 11 < n_ch ≤ 20, (c) 21 < n_ch ≤ 30, (d) 31 < n_ch ≤ 40, (e) 41 < n_ch ≤ 50, (f) 51 < n_ch ≤ 60, (g) 61 < n_ch ≤ 70, (h) 71 < n_ch ≤ 80 and (i) 81 < n_ch ≤ 90. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 91 < n_ch ≤ 100, (b) 101 < n_ch ≤ 125, (c) 126 < n_ch ≤ 150, (d) 151 < n_ch ≤ 200, (e) 201 < n_ch ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 91 < n_ch ≤ 100, (b) 101 < n_ch ≤ 125, (c) 126 < n_ch ≤ 150, (d) 151 < n_ch ≤ 200, (e) 201 < n_ch ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 91 < n_ch ≤ 100, (b) 101 < n_ch ≤ 125, (c) 126 < n_ch ≤ 150, (d) 151 < n_ch ≤ 200, (e) 201 < n_ch ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 91 < n_ch ≤ 100, (b) 101 < n_ch ≤ 125, (c) 126 < n_ch ≤ 150, (d) 151 < n_ch ≤ 200, (e) 201 < n_ch ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 91 < n_ch ≤ 100, (b) 101 < n_ch ≤ 125, (c) 126 < n_ch ≤ 150, (d) 151 < n_ch ≤ 200, (e) 201 < n_ch ≤ 250. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 101 < n_ch ≤ 110, (b) 111 < n_ch ≤ 120, (c) 121 < n_ch ≤ 130, (d) 131 < n_ch ≤ 140, (e) 141 < n_ch ≤ 155, (f) 156 < n_ch ≤ 175, (g) 176 < n_ch ≤ 200, (h) 201 < n_ch ≤ 230 and (i) 231 < n_ch ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 101 < n_ch ≤ 110, (b) 111 < n_ch ≤ 120, (c) 121 < n_ch ≤ 130, (d) 131 < n_ch ≤ 140, (e) 141 < n_ch ≤ 155, (f) 156 < n_ch ≤ 175, (g) 176 < n_ch ≤ 200, (h) 201 < n_ch ≤ 230 and (i) 231 < n_ch ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 101 < n_ch ≤ 110, (b) 111 < n_ch ≤ 120, (c) 121 < n_ch ≤ 130, (d) 131 < n_ch ≤ 140, (e) 141 < n_ch ≤ 155, (f) 156 < n_ch ≤ 175, (g) 176 < n_ch ≤ 200, (h) 201 < n_ch ≤ 230 and (i) 231 < n_ch ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 101 < n_ch ≤ 110, (b) 111 < n_ch ≤ 120, (c) 121 < n_ch ≤ 130, (d) 131 < n_ch ≤ 140, (e) 141 < n_ch ≤ 155, (f) 156 < n_ch ≤ 175, (g) 176 < n_ch ≤ 200, (h) 201 < n_ch ≤ 230 and (i) 231 < n_ch ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 101 < n_ch ≤ 110, (b) 111 < n_ch ≤ 120, (c) 121 < n_ch ≤ 130, (d) 131 < n_ch ≤ 140, (e) 141 < n_ch ≤ 155, (f) 156 < n_ch ≤ 175, (g) 176 < n_ch ≤ 200, (h) 201 < n_ch ≤ 230 and (i) 231 < n_ch ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 101 < n_ch ≤ 110, (b) 111 < n_ch ≤ 120, (c) 121 < n_ch ≤ 130, (d) 131 < n_ch ≤ 140, (e) 141 < n_ch ≤ 155, (f) 156 < n_ch ≤ 175, (g) 176 < n_ch ≤ 200, (h) 201 < n_ch ≤ 230 and (i) 231 < n_ch ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 101 < n_ch ≤ 110, (b) 111 < n_ch ≤ 120, (c) 121 < n_ch ≤ 130, (d) 131 < n_ch ≤ 140, (e) 141 < n_ch ≤ 155, (f) 156 < n_ch ≤ 175, (g) 176 < n_ch ≤ 200, (h) 201 < n_ch ≤ 230 and (i) 231 < n_ch ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 101 < n_ch ≤ 110, (b) 111 < n_ch ≤ 120, (c) 121 < n_ch ≤ 130, (d) 131 < n_ch ≤ 140, (e) 141 < n_ch ≤ 155, (f) 156 < n_ch ≤ 175, (g) 176 < n_ch ≤ 200, (h) 201 < n_ch ≤ 230 and (i) 231 < n_ch ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 101 < n_ch ≤ 110, (b) 111 < n_ch ≤ 120, (c) 121 < n_ch ≤ 130, (d) 131 < n_ch ≤ 140, (e) 141 < n_ch ≤ 155, (f) 156 < n_ch ≤ 175, (g) 176 < n_ch ≤ 200, (h) 201 < n_ch ≤ 230 and (i) 231 < n_ch ≤ 300. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), for the high-multiplicity track (HMT) events using the unlike-charge particle (UCP) pairs reference sample for k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample for n_ch - intervals∶ (a) 2 < n_ch ≤ 9, (b) 10 < n_ch ≤ 18, (c) 19 < n_ch ≤ 27, (d) 28 < n_ch ≤ 36, (e) 37 < n_ch ≤ 45, (f) 46 < n_ch ≤ 54, (g) 55 < n_ch ≤ 63, (h) 64 < n_ch ≤ 72, (i) 73 < n_ch ≤ 81, (j) 82 < n_ch ≤ 90, (k) 91 < n_ch ≤ 113, and (l) 114 < n_ch ≤ 136. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The single-ratio two-particle correlation functions, C₂^data(Q), at 7 TeV for the minimum-bias (MB) events using the unlike-charge particle (UCP) pairs reference sample k_T - intervals∶ (a) 100 < k_T ≤ 200 MeV, (b) 200 < k_T ≤ 300 MeV, (c) 300 < k_T ≤ 400 MeV, (d) 400 < k_T ≤ 500 MeV, (e) 500 < k_T ≤ 600 MeV, (f) 600 < k_T ≤ 700 MeV, (g) 700 < k_T ≤ 1000 MeV, and (h) 1000 < k_T ≤ 1500 MeV. The error bars represent the statistical uncertainties. The boxes represent the systematic uncertainties, which are the sum in quadrature of a variation of the Coulomb correction, the track reconstruction efficiency and the unfolding matrix.

The correlation strength, λ, and source radius, R, of the exponential fits to the two-particle double-ratio correlation functions, R₂(Q), in dependence on the multiplicity, m_ch, intervals for the minimum-bias (MB) and the high-multiplicity track (HMT) events for p_T > 100 MeV at √s = 13 TeV. Statistical uncertainties for √χ²/ndf>1 are corrected by the √χ²/ndf. The total uncertainties are shown.

The correlation strength, λ, and source radius, R, of the exponential fits to the two-particle double-ratio correlation functions, R₂(Q), in dependence on the multiplicity, m_ch, intervals for the minimum-bias (MB) and the high-multiplicity track (HMT) events for p_T > 500 MeV at √s = 13 TeV. Statistical uncertainties for √χ²/ndf>1 are corrected by the √χ²/ndf. The total uncertainties are shown.

The correlation strength, λ, and source radius, R, of the exponential fits to the two-particle double-ratio correlation functions, R₂(Q), in dependence on the pair transverse momentum, k_T, intervals for the minimum-bias (MB) and the high-multiplicity track (HMT) events for p_T > 100 MeV at √s = 13 TeV. Statistical uncertainties for √χ²/ndf>1 are corrected by the √χ²/ndf. The total uncertainties are shown.

The correlation strength, λ, and source radius, R, of the exponential fits to the two-particle double-ratio correlation functions, R₂(Q), in dependence on the pair transverse momentum, k_T, intervals for the minimum-bias (MB) and the high-multiplicity track (HMT) events for p_T > 500 MeV at √s = 13 TeV. Statistical uncertainties for √χ²/ndf>1 are corrected by the √χ²/ndf. The total uncertainties are shown.

Breakdown of expected and observed yields in the electroweak search Int, Low, and OffShell signal regions after a simultaneous fit to the signal regions and control regions. All statistical and systematic uncertainties are included.

Breakdown of expected and observed yields in the four edge signal regions, integrated over the $m_{\ell\ell}$ distribution after a separate simultaneous fit to each signal region and control region pair. The uncertainties include both the statistical and systematic sources.

Breakdown of expected and observed yields in the three on-$Z$ signal regions after a separate simultaneous fit to each signal region and control region pair. The uncertainties include both the statistical and systematic sources.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-High-Sideband-EWK (top-left), VR-High-R-EWK (top-right), VR-1J-High-EWK (bottom-left), and VR-$\ell\ell bb$-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Distributions of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in VR-Int-EWK (top-left), VR-Low-EWK (top-right), VR-Low-2-EWK (bottom-left), and VR-OffShell-EWK (bottom-right) from the EWK search after a simultaneous fit of the control regions. The hatched band includes both the systematic and statistical uncertainties. The last bin includes the overflow.

Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.

Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.

Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.

Observed and expected dilepton mass distributions in VRC-STR (top-left), VRLow-STR (top-right), VRMed-STR (bottom-left), and VRHigh-STR (bottom-right). Each validation region is fit separately with the corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The entries are normalized to the bin width, and the last bin is the overflow.

Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.

Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.

Observed and expected jet multiplicity in VRLow-STR (top-left), VRMed-STR (top-right), and VRHigh-STR (bottom) after a fit performed on the $m_{\ell\ell}$ distribution and corresponding control region. All statistical and systematic uncertainties are included in the hatched band. The last bin contains the overflow.

Observed and expected dilepton mass distributions in VR3L-STR without a fit to the data. The 'Other' category includes the negligible contributions from $t\bar{t}$ and $Z/\gamma^*$+jets processes. The hatched band contains the statistical uncertainty and the theoretical systematic uncertainties of the $WZ$/$ZZ$ prediction, which are the dominant sources of uncertainty. No fit is performed. The last bin contains the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected distributions in five EWK search regions after a simultaneous fit to the SR and CR. In the top row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-High_8-EWK and $m_{bb}$ in SR-$\ell\ell bb$-EWK. In the middle row, left-to-right, are $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Int-EWK and $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ in SR-Low-EWK. In the bottom row is $m_{\ell\ell}$ in SR-OffShell-EWK. Overlaid are example C1N2 and GMSB signal models, where the numbers in the brackets indicate the masses, in $\mathrm{GeV}$, of the $\tilde{\chi}_1^\pm$ and $\tilde{\chi}_2^0$ or the mass of the $\tilde{\chi}_1^0$ and branching ratio to the Higgs boson respectively. All statistical and systematic uncertainties are included in the hatched bands. The last bin includes the overflow.

Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.

Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.

Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.

Observed and expected dilepton mass distributions in SRC-STR (top-left), SRLow-STR (top-right), SRMed-STR (bottom-left), and SRHigh-STR (bottom-right), with the binning used for interpretations after a separate simultaneous fit to each signal region and control region pair. The red dashed lines are example signal models overlaid on the figure. All statistical and systematic uncertainties are included in the hatched bands. The last bins are the overflow.

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294].

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294]. The grey numbers indicate the observed 95\% CLs upper limit on the cross section.

Expected and observed exclusion contours from the EWK analysis for the C1N2 model (left) and GMSB model (right). The dashed line indicates the expected limits at 95$\%$ CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties on the background prediction and experimental uncertainties on the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The gray shaded areas indicate observed limits on these models from the two lepton channels of Ref.~[arXiv: 1803.02762] and Ref.~[arXiv: 1403.5294]. The grey numbers indicate the observed 95$\%$ CLs upper limit on the cross section.

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$ ilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].The grey numbers indicated the observed 95\% CL upper limit on the cross section.

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

Expected and observed exclusion contours derived from the combination of all of the Strong search SRs for the $\tilde{g}$--$\tilde{\ell}$ (top-left), $\tilde{g}$--$Z$ (top-right), and $\tilde{s}--Z$ (bottom) models. The dashed line indicates the expected limits at 95\% CL and the surrounding band shows the $1\sigma$ variation of the expected limit as a consequence of the uncertainties in the background prediction and experimental uncertainties of the signal ($\pm1\sigma_\mathrm{exp}$). The red dotted lines surrounding the observed limit contours indicate the variation resulting from changing the signal cross-section within its uncertainty ($\pm1\sigma_\mathrm{theory}^\mathrm{SUSY}$). The grey-shaded area indicates the observed limits on these models from Ref. [23].

The combined $E_{\mathrm{T}}^{\mathrm{miss}}$ distribution of VRLow-STR and SRLow-STR (left), and the same region with the $\Delta\phi(\boldsymbol{j}_{1,2},\boldsymbol{\mathit{p}}_{ ext{T}}^{ ext{miss}})<0.4$ requirement, used as a control region to normalize the $Z/\gamma^*+\mathrm{jets}$ process (right). Separate fits for the SR and VR are performed, as for the results in the paper, and the resulting distributions are merged. All statistical and systematic uncertainties are included in the hatched bands. The last bins contain the overflow.

Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.

Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.

Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.

Cutflow of expected events in the four Strong search edge signal regions. `Leptons' refers to electrons and muons only. The gluino-$Z^{(*)}$ model with $m_{ ilde{g}}=800~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRC-STR with 60000 Monte Carlo (MC) events generated. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for SRMed-STR with 30000 (MC) events generated. The gluino-slepton model with $m_{ ilde{g}}=2~TeV$ and $m_{ ilde{\ell}}=1.3~TeV$ is used for SRLow-STR and SRHigh-STR with 30000 MC events generated. The Generator Filter requires two 5~GeV leptons and 100~GeV of \met. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~GeV$ or at least one lepton with $p_{\mathrm{T}}>25~GeV$ and a photon with $p_{\mathrm{T}}>40~GeV$, with all objects within $|\eta|=2.6$.

Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.

Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.

Cutflow of expected events in the three Strong search on-$Z$ signal regions. The cutflow up to the signal region specific requirements is the same as in the Strong search edge cutflow. The slepton-$Z^{(*)}$ model with $m_{ ilde{\ell}}=1200~GeV$ and $m_{ ilde{\chi}_1^0}=700~GeV$ is used for all of the on-$Z$ signal regions with 30000 (MC) events generated.

Table 36: Cutflow of expected events in the region SR-OffShell_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 36: Cutflow of expected events in the region SR-OffShell_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 37: Cutflow of expected events in the region SR-OffShell_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 37: Cutflow of expected events in the region SR-OffShell_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 38: Cutflow of expected events in the region SR-Low_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 38: Cutflow of expected events in the region SR-Low_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 39: Cutflow of expected events in the region SR-Low_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 39: Cutflow of expected events in the region SR-Low_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 40: Cutflow of expected events in the region SR-Low-2-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 40: Cutflow of expected events in the region SR-Low-2-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 41: Cutflow of expected events in the region SR-Int_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 41: Cutflow of expected events in the region SR-Int_a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 42: Cutflow of expected events in the region SR-Int_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 42: Cutflow of expected events in the region SR-Int_b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 43: Cutflow of expected events in the region SR-High_16a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 43: Cutflow of expected events in the region SR-High_16a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 44: Cutflow of expected events in the region SR-High_16b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 44: Cutflow of expected events in the region SR-High_16b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 45: Cutflow of expected events in the region SR-High_8a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 45: Cutflow of expected events in the region SR-High_8a-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 46: Cutflow of expected events in the region SR-High_8b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 46: Cutflow of expected events in the region SR-High_8b-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 47: Cutflow of expected events in the region SR-1J-High-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 47: Cutflow of expected events in the region SR-1J-High-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 48: Cutflow of expected events in the region SR-llbb-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

Table 48: Cutflow of expected events in the region SR-llbb-EWK. Requirements below the line are specific to this region. On the Generator Filter line, the total number of unweighted events simulated is given in brackets. `Leptons' refers to electrons and muons only. For C1N2 models, the Generator Filter requires at least two $7~\mathrm{GeV}$ leptons and for C1N2 models with mass splittings below the Z boson mass it also requires $75~\mathrm{GeV}$ of $E_{\mathrm{T}}^{\mathrm{miss}}$. For GMSB models, the Generator Filter requires at least two $3~\mathrm{GeV}$ leptons. For on-shell C1N2 models, the `Forced Decays' require each Z boson to decay to a charged lepton pair (electron, muon, or tau) and each W boson to decay hadronically. For off-shell C1N2 models, each neutralino is forced to produce a charged lepton pair in its decay, and each chargino can produce any fermion pair. The SUSY2 kernel requires at least two leptons with $p_{\mathrm{T}}>9~\mathrm{GeV}$ or at least one lepton with $p_{\mathrm{T}}>25~\mathrm{GeV}$ and a photon with $p_{\mathrm{T}}>40~\mathrm{GeV}$, with all objects within $|\eta|=2.6$.

The combined $m_{jj}$ distribution of CR-Z-EWK and SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in CR-Z-met-EWK (right), which removes the upper limit of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}}) < 9$ from the definition of CR-Z-EWK. This $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ tail overlaps other control and validation regions, but not signal regions. The arrows indicate the signal region SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ phase space which is not included in CR-Z-EWK (right). All EWK search control and signal regions are included in the fit. All statistical and systematic uncertainties are included in the hatched bands. The theoretical uncertainties from CR-Z-EWK are applied to CR-Z-met-EWK. The last bins contain the overflow.

The combined $m_{jj}$ distribution of CR-Z-EWK and SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ distribution in CR-Z-met-EWK (right), which removes the upper limit of $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}}) < 9$ from the definition of CR-Z-EWK. This $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ tail overlaps other control and validation regions, but not signal regions. The arrows indicate the signal region SR-Low-EWK (left), and the $\mathcal{S}(E_{\mathrm{T}}^{\mathrm{miss}})$ phase space which is not included in CR-Z-EWK (right). All EWK search control and signal regions are included in the fit. All statistical and systematic uncertainties are included in the hatched bands. The theoretical uncertainties from CR-Z-EWK are applied to CR-Z-met-EWK. The last bins contain the overflow.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the GMSB model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-OffShell-EWK and SR-Low-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-Low-2-EWK and SR-Int-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-High-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) for the C1N2 model in the regions SR-1J-High-EWK and SR-$\ell\ell bb$-EWK. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. For models with mass splittings below the Z boson mass, this filter also requires $E_{\mathrm{T}}^{\mathrm{miss}} > 75~\mathrm{GeV}$. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_SLN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the GG_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

Signal region acceptance (left) and efficiency (right) over the full \mll\ range for the SS_N2_ZN1 model in Strong search regions. Acceptance is calculated by applying the signal-region requirements to particle-level objects, which do not suffer from identification inefficiencies or mismeasurements. The efficiency is calculated with fully reconstructed objects with the acceptance divided out.

The observed mass distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

List of expected and observed events, $p_{0}$-value and the corresponding $Z$ local significance, as well as the 95% CLs upper limit of the expected and observed signal events ($S^{95}_ ext{exp} and $S^{95}_ ext{obs}$ ) in each mass window for SR-Inclusive bins of the short lifetime regime.

List of expected and observed events, $p_{0}$-value and the corresponding $Z$ local significance, as well as the 95% CLs upper limit of the expected and observed signal events ($S^{95}_ ext{exp} and $S^{95}_ ext{obs}$ ) in each mass window for SR-Inclusive bins of the long lifetime regime.

The observed $p_{\rm T$ distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $|\eta|$ distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $|\eta|$ distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Inclusive_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Inclusive_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Trk-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Mu-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Trk-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Mu-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Trk-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed mass distribution in the SR-Mu-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.

Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.

Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.

Lower limits on the gluino mass, from gluino $R$-hadron pair production, as a function of gluino lifetime for two neutralino mass assumptions of (a) $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$ and (b) $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$. The upper $1 \sigma_\text{exp}$ expected bound is very close to the expected limit for some lifetime values due to the expected background getting very close to 0 events.

(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.

(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.

(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.

(a) Lower limits on the chargino mass as a function of lifetime, and (b) the contours around the excluded mass-lifetime region for stau pair production.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Trk-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Mu-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Trk-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Mu-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Trk-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-LowPt-Mu-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Trk-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Mu-IBL0_Low. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Trk-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Mu-IBL0_High. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Trk-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

Comparison of the observed and expected VAR distributionsin VR-HiEta-Mu-IBL1. The band on the expected background estimation indicates the total uncertainty of the estimation. Downward triangle markers at the bottom of the panels indicate there is no events observed in the corresponding bin, while upward triangle markers at the bottom panel indicate the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Trk-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Mu-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Trk-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Mu-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Trk-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed $p_{\rm T$ distribution in the SR-Mu-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Trk-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Mu-IBL0_Low signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Trk-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Mu-IBL0_High signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Trk-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

The observed dE/dx distribution in the SR-Mu-IBL1 signal-region bin. The band on the expected background indicates the total uncertainty of the estimation. Several representative signal models are overlaid. Events outside the shown range are accumulated in the rightmost bin indicated as 'Overflow'. Downward triangle markers at the bottom of the panels indicate that no events are observed in the corresponding mass bin, while upward triangle markers in the lower panels indicate that the observed data is beyond the range.

Expected and observed distributions in SR-Inclusive_Low of missing transverse momentum. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of missing transverse momentum. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of relative phi-angle between pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of relative phi-angle between pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the transverse mass of pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the transverse mass of pTmiss and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the relative phi-angle between the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track, and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the relative phi-angle between the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track, and the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the relative phi-angle between pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the relative phi-angle between pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the transverse mass of pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the transverse mass of pTmiss and the leading jet pT, required to be separated by at least deltaR > 0.4 with respect to the signal candidate track. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_Low of the effective mass, defined as the scalar sum pT of the signal candidate track, jets satisfying pT > 30 GeV, excluding ones within deltaR < 0.4 with respect to the signal candidate track, and pTmiss. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

Expected and observed distributions in SR-Inclusive_High of the effective mass, defined as the scalar sum pT of the signal candidate track, jets satisfying pT > 30 GeV, excluding ones within deltaR < 0.4 with respect to the signal candidate track, and pTmiss. The expected background distribution is calculated for each |eta| slice using CR-kin control region as the template and applying the scale factor using the dE/dx distribution in CR-dEdx of the corresponding |eta| slice. The last bins of the plots include overflow events above the range.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $m(\tilde{\chi}_{1}^{0}) = 100 \text{GeV}$, with lifetime with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.

The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.

The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.

The expected upper limits on cross-section for gluinos with $\Delta m(\tilde{g}, \tilde{\chi}_{1}^{0}) = 30 \text{GeV}$, with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, and (d) 30 ns.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for charginos with lifetime (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

The expected upper limits on cross-section for sleptons with lifetime (a) 1 ns, (b) 3 ns, (c) 10 ns, (d) 30 ns, and (e) stable.

Muon reconstruction efficiency as a function of β and |η| for (a) stable charginos and (b) stable charged R-hadrons. For weakly interacting LLPs with calorimeter materials the efficiency for the chargino is recommended to refer to. The muon reconstruction efficiency for R-hadrons is significantly lower due to having QCD interactions with materials.

Muon reconstruction efficiency as a function of β and |η| for (a) stable charginos and (b) stable charged R-hadrons. For weakly interacting LLPs with calorimeter materials the efficiency for the chargino is recommended to refer to. The muon reconstruction efficiency for R-hadrons is significantly lower due to having QCD interactions with materials.

Trigger and event selection efficiencies. The band on the marker indicates a typical size of fluctuation by the LLP mass and lifetime observed by the samples used in efficiency derivation, but it does not indicate the full envelope of model dependence.

Trigger and event selection efficiencies. The band on the marker indicates a typical size of fluctuation by the LLP mass and lifetime observed by the samples used in efficiency derivation, but it does not indicate the full envelope of model dependence.

Signal track selection efficiency as a function of CLLP $\beta\gamma$ for SR-Inclusive_Low and SR-Inclusive_High bins. The band on the marker indicates a typical size of fluctuation by the LLP mass and lifetime observed by the samples used in efficiency derivation, but it does not indicate the full envelope of model dependence.

Signal selection efficiency by the mass window for SR-Inclusive_Low and SR-Inclusive_High bins.

Acceptance for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |\eta| < 1.8 and r_decay > 500 mm.

Acceptance for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |eta| < 1.8 and r_decay > 500 mm.

Acceptance for the chargino pair-production model for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |\eta| < 1.8 and r_decay > 500 mm.

Acceptance for the stau pair-production model for various masses and lifetimes. The acceptance is defined as the fraction of events having at least one charged LLP satisfying pT > 120 GeV, |\eta| < 1.8 and r_decay > 500 mm.

Event-level efficiency for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.

Event-level efficiency for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.

Event-level efficiency for the chargino pair-production model for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.

Event-level efficiency for the stau pair-production model for various masses and lifetimes. The efficiency is defined as the fraction of events satisfying the selection of trigger, event and jet cleaning, ETmiss and primary vertex requirements per events satisfying the acceptance criteria.

Efficiency of SR-Inclusive_Highfor the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Highfor the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Highfor the chargino pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Highfor the stau pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Low for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Low for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Low for the chargino pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Efficiency of SR-Inclusive_Low for the stau pair-production model for various masses and lifetimes. The efficiency is defined as the ratio of events satisfying the signal region selection to those satisfying the acceptance criteria. The mass window is not applied for the presented numbers.

Passing events in event selection steps for the R-hadron pair-production model with m(N1) = 100 GeV for various masses and lifetimes.

Passing events in event selection steps for the R-hadron pair-production model with DeltaM(gluino, N1) = 30 GeV for various masses and lifetimes.

Passing events in event selection steps for the chargino pair-production model for various masses and lifetimes.

Passing events in event selection steps for the stau pair-production model for various masses and lifetimes.

Measured differential cross section with associated uncertainties as a function of $N$($b$-jets). Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate. For the meaning of the bins please refer to the figure.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{j1}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>30\ \mathrm{GeV}}=0)$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $m_{jj}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $\Delta\phi_{jj}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma}$ in bins of $|y_{\gamma\gamma}|$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Measured differential cross section with associated uncertainties as a function of $\Delta\phi_{jj}$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Observed statistical correlations, evaluated with a bootstrapping technique, between $p_\mathrm{T}^{\gamma\gamma}$, $N_\mathrm{jets}$, $m_{jj}$, $\Delta\phi_{jj}$, and $p_\mathrm{T}^{j1}$

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma}$.

Post-fit correlation matrix for the differential cross section measured as a function of $N_\mathrm{jets}$.

Post-fit correlation matrix for the differential cross section measured as a function of $N$($b$-jets).

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{j1}$.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>30\ \mathrm{GeV}}=0)$.

Post-fit correlation matrix for the differential cross section measured as a function of $m_{jj}$.

Post-fit correlation matrix for the differential cross section measured as a function of $\Delta\phi_{jj}$.

Post-fit correlation matrix for the differential cross section measured as a function of $\Delta\phi_{jj}$ in the VBF fiducial region.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma}$ in bins of $|y_{\gamma\gamma}|$. The bins are the same as in the corresponding plot of the differential cross section.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma1}/m_{\gamma\gamma}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma1}/m_{\gamma\gamma}$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma2}/m_{\gamma\gamma}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma2}/m_{\gamma\gamma}$.

Measured differential cross section with associated uncertainties as a function of $|y_{\gamma\gamma}|$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $|y_{\gamma\gamma}|$.

Measured differential cross section with associated uncertainties as a function of $m_{\gamma\gamma j}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $m_{\gamma\gamma j}$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma j}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma j}$.

Measured differential cross section with associated uncertainties as a function of $H_{T}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $H_{T}$.

Measured differential cross section with associated uncertainties as a function of $\tau_{C,j1}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $\tau_{C,j1}$.

Measured differential cross section with associated uncertainties as a function of $\sum\tau_{C,j}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $\sum\tau_{C,j}$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>40\ \mathrm{GeV}}=0)$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>40\ \mathrm{GeV}}=0)$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>50\ \mathrm{GeV}}=0)$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>50\ \mathrm{GeV}}=0)$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>60\ \mathrm{GeV}}=0)$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma} (N_\mathrm{jets}^{p_T>60\ \mathrm{GeV}}=0)$.

Measured differential cross section with associated uncertainties as a function of $\pi - |\Delta\phi_{\gamma\gamma,jj}|$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $\pi - |\Delta\phi_{\gamma\gamma,jj}|$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma jj}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma jj}$.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma}$ in bins of $p_{T}^{\gamma\gamma j}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma}$ in bins of $p_{T}^{\gamma\gamma j}$. The bins are the same as in the corresponding plot of the differential cross section.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma}$ in bins of $\tau_{C,j1}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma}$ in bins of $\tau_{C,j1}$. The bins are the same as in the corresponding plot of the differential cross section.

Measured differential cross section with associated uncertainties as a function of $(p_{T}^{\gamma1}-p_{T}^{\gamma2})/m_{\gamma\gamma}$ in bins of $(p_{T}^{\gamma1}+p_{T}^{\gamma2})/m_{\gamma\gamma}$. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $(p_{T}^{\gamma1}-p_{T}^{\gamma2})/m_{\gamma\gamma}$ in bins of $(p_{T}^{\gamma1}+p_{T}^{\gamma2})/m_{\gamma\gamma}$. The bins are the same as in the corresponding plot of the differential cross section.

Measured differential cross section with associated uncertainties as a function of $|\eta^*|$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $|\eta^*|$ in the VBF fiducial region.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{\gamma\gamma jj}$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{\gamma\gamma jj}$ in the VBF fiducial region.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{j1}$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{j1}$ in the VBF fiducial region.

Measured differential cross section with associated uncertainties as a function of $p_{T}^{j1}$ in bins of $\Delta\phi_{jj}$ in the VBF fiducial region. Each systematic uncertainty source is fully uncorrelated with the other sources and fully correlated across bins, except for the background modelling systematics for which an uncorrelated treatment across bins is more appropriate.

Post-fit correlation matrix for the differential cross section measured as a function of $p_{T}^{j1}$ in bins of $\Delta\phi_{jj}$ in the VBF fiducial region. The bins are the same as in the corresponding plot of the differential cross section.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Normalized differential cross section, $(1/\sigma_\text{fid})d\sigma_\text{fid}/d\log(m_{bb}/p_\text{T})$, as a function of $\log(m_{bb}/p_\text{T})$ for $m_{bb}$ the invariant mass of the two b-jets.