Parametrization of the $A_{X}$ factor, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level, as function of $m_{X}$ obtained from the narrow width simulated signal samples produced in gluon fusion.

The correction factor, $C_{X}$, defined as the ratio between the number of reconstructed signal events passing the analysis cuts and the number of signal events at the particle level generated within the fiducial volume, and acceptance correction factor, $A_{X}$, defined as the fraction of diphoton resonances satisfying the fiducial acceptance at the particle level. Both are computed for NWA spin-0 models as a function of $m_{X}$.

Effect of event selections on a scalar MC signal sample generated for $m_{X}$ = 15 GeV and on the data. For the MC sample, the efficiencies are shown after applying event weights and a truth level filter that requires two photons with $p^{\gamma\gamma}_{T}>40$ GeV; for the data, the absolute yields are shown. The initial yields for data include a trigger preselection that is the OR of a list of single photon and diphoton triggers. The "2 $loose$ photons" step includes the kinematic acceptance cuts.

Parameterization of the Double Sided Crystal Ball function parameters describing the scalar mass resolution model as a function of $m_{X}$ [GeV].

Acceptance times efficiency as a function of resonant mass for each event selection step in the search for a neutral heavy scalar resonance produced in association with a W boson decaying to a charged lepton and a neutrino.

Acceptance times efficiency as a function of resonant mass for each event selection step in the search for a neutral heavy scalar resonance produced in association with a Z boson decaying to charged leptons.

Acceptance times efficiency as a function of resonant mass for each event selection step in the search for a neutral heavy scalar resonance produced in association with a Z boson decaying to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to neutrinos.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy narrow-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 260 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 300 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Acceptance times efficiency as a function of pseudoscalar resonant mass for each event selection step in the search for a neutral heavy large-width pseudoscalar resonance that decays to a 400 GeV scalar resonance and a Z boson, which decays to charged leptons.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a W boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a W boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a Z boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected and observed 95% CL upper limits on the cross-section of resonant $H\to 4b$ production in association with a Z boson. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Observed 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Observed 95% CL upper limits on the cross-section of a heavy narrow-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Expected 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Observed 95% CL upper limits on the cross-section of a heavy large-width pseudoscalar resonance decaying to a Z boson and a heavy scalar resonance decaying to $H\to 4b$. The $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty ranges for the expected limits are shown.

Data and post-fit signal and background from S+B fit for 315 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 315 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a W boson.

Data and post-fit signal and background from S+B fit for 550 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for 550 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for 400 GeV resonant $H\to 4b$ production in association with a Z boson.

Data and post-fit signal and background from S+B fit for a 790 GeV narrow-width pseudoscalar resonance decaying to a Z boson and a 300 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 790 GeV narrow-width pseudoscalar resonance decaying to a Z boson and a 300 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 420 GeV large-width pseudoscalar resonance decaying to a Z boson and a 320 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 420 GeV large-width pseudoscalar resonance decaying to a Z boson and a 320 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 700 GeV large-width pseudoscalar resonance decaying to a Z boson and a 380 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for a 700 GeV large-width pseudoscalar resonance decaying to a Z boson and a 380 GeV scalar resonance decaying to $H\to 4b$.

Data and post-fit signal and background from S+B fit for SM VHH production, with each Higgs boson decaying to $2b$.

Data and post-fit signal and background from S+B fit for SM VHH production, with each Higgs boson decaying to $2b$.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-0J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[100,105)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[105,110)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[110,115)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[115,120)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[120,125)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[125,130)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[130,140)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the slepton pair production model, in the SR-1J $m_{\mathrm{T2}}^{100} \in[140,\infty)$ region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-0J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.

Cutflow table for the slepton signal sample with $m(\tilde{\ell},\tilde{\chi}_1^0) = (100,70)$ GeV, in the SR-1J $m_{\mathrm{T2}}^{100} \in [100,\infty)$ region. The yields include the process cross section and are weighted to the 139 fb$^{-1}$ luminosity. 246000 events were generated for the sample.

Observed and expected exclusion limits on SUSY simplified models, with observed upper limits on signal cross-section (fb) overlaid, for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the (a) $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\ell})-\Delta m(\tilde{\ell},\tilde{\chi}_1^0)$ planes. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP for $\tilde{\mu}_{\textup{R}}$ and by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for direct selectron production in the (a) $m(\tilde{e})-m(\tilde{\chi}_1^0)$ and (c) $m(\tilde{e})-\Delta m(\tilde{e},\tilde{\chi}_1^0)$ planes, and for direct smuon production in the (b) $m(\tilde{\mu})-m(\tilde{\chi}_1^0)$ and (d) $m(\tilde{\mu})-\Delta m(\tilde{\mu},\tilde{\chi}_1^0)$ planes. In Figure (a) and (c) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{e}_{\textup{L,R}}$ and for $\tilde{e}_{\textup{L}}$ and $\tilde{e}_{\textup{R}}$. In Figure (b) and (d) the observed (solid thick lines) and expected (dashed lines) exclusion contours are indicated for combined $\tilde{\mu}_{\textup{L,R}}$ and for $\tilde{\mu}_{\textup{L}}$. No unique sensitivity to $\tilde{\mu}_{\textup{R}}$ is observed. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown in the shaded areas.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

Observed and expected exclusion limits on SUSY simplified models for slepton-pair production in the $m(\tilde{\ell})-m(\tilde{\chi}_1^0)$ plane. Only $\tilde{e}$ and $\tilde{\mu}$ are considered. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The red contour shows the exclusion limits obtained using both the SR-0J and SR-1J region, as presented in Figure 6. The blue and green contours correspond to the result obtained considering only SR-0J and SR-1J region respectively. All limits are computed at 95% CL. The observed limits obtained by the ATLAS experiment in previous searches are also shown.

The upper panel shows the observed number of events in each of the binned SRs defined in Table 3, together with the expected SM backgrounds obtained after applying the efficiency correction method to compute the number of expected FSB events. `Others' include the non-dominant background sources, e.g. $t \bar{t}$+$V$, Higgs boson and Drell--Yan events. The uncertainty band includes systematic and statistical errors from all sources. The distributions of two signal points with mass splittings $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 30$ GeV and $\Delta m(\tilde{\ell},\tilde{\chi}_1^0) = m(\tilde{\ell})-m(\tilde{\chi}_1^0) = 50$ GeV are overlaid. The lower panel shows the significance as defined in Ref. [115].

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR$^{\text{-DF BDT-signal}\in(0.81,1]}_{\text{-SF BDT-signal}\in(0.77,1]}$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.81,0.8125]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8125,0.815]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.815,0.8175]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8175,0.82]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.82,0.8225]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8225,0.825]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.825,0.8275]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8275,0.83]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.83,0.8325]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8325,0.835]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.835,0.8375]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.8375,0.84]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.84,0.845]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.845,0.85]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.85,0.86]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-DF BDT-signal$\in(0.86,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.77,0.775]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.775,0.78]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.78,0.785]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.785,0.79]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.785,0.79]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,0.795]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.79,0.795]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.795,0.80]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.795,0.80]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,0.81]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.80,0.81]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.81,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

The figure shows the signal acceptance (a) and efficiency (b) plots for the $\tilde{\chi}_1^+\tilde{\chi}_1^-$ production with $W$-boson-mediated decay model, in the SR-SF BDT-signal$\in(0.81,1]$ inclusive region. 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. Large acceptance and efficiency differences in neighbouring points are due to statistical fluctuations.

Cutflow table for the chargino signal sample with $m\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0=(125,25)$ GeV, in the SR-SF BDT-signal$\in (0.77,1]$ and SR-DF BDT-signal$\in (0.81,1]$ regions. The yields include the process cross-section and are weighted to the 139 fb$^{-1}$ luminosity. 170000 events were generated for the sample.

Observed and expected exclusion limits on SUSY simplified models, with observed upper limits on signal cross-section (fb) overlaid, for chargino-pair production with $W$-boson-mediated decays in the $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ plane. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

Observed and expected exclusion limits on SUSY simplified models for chargino-pair production with $W$-boson-mediated decays in the (a) $m(\tilde{\chi}_1^{\pm})-m(\tilde{\chi}_1^0)$ and (b) $m(\tilde{\chi}_1^{\pm})-\Delta m(\tilde{\chi}_1^{\pm},\tilde{\chi}_1^0)$ planes. The observed (solid thick line) and expected (thin dashed line) exclusion contours are indicated. The shaded band around the dashed line corresponds to the $\pm 1 \sigma$ variations in the expected limit, including all uncertainties except theoretical uncertainties in the signal cross-section. The dotted lines around the observed limit illustrate the change in the observed limit as the nominal signal cross-section is scaled up and down by the theoretical uncertainty. All limits are computed at 95% CL. The observed limits obtained at LEP and by the ATLAS experiment in previous searches are also shown. In case of the search performed on ATLAS Run 1 data at $\sqrt{s} = 8$ TeV no sensitivity was expected for the exclusion in the mass plane.

The upper panel shows the observed number of events in the SRs defined in Table 3, together with the expected SM backgrounds obtained after the background fit in the CRs. `Others' include the non-dominant background sources, e.g.$t \bar{t}$+$V$, Higgs boson and Drell--Yan events. The uncertainty band includes systematic and statistical errors from all sources. Distributions for three benchmark signal points are overlaid for comparison. The lower panel shows the significance as defined in Ref. [115].

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the hadhad category. The efficiencies are calculated with respect to HH to bbtautau decays in which both tau-leptons decay hadronically. The ‘Pre-selection’ consists of basic requirements, including that at least two tau-had-vis pass loose kinematic requirements, at least one of the tau-had-vis candidate passes identification requirement, and that events do not contain an electron or muon. The ‘Object selections’ require exactly two tau-had-vis candidates, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Loose tau ID’ requires that both tau-had-vis candidates pass identification requirements. The ‘DTT offline jet cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad SLT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Cumulative efficiency for simulated signal events to pass each stage of the event selection in the lephad LTT category. The efficiencies are calculated with respect to HH to bbtautau decays in which one tau-lepton decays hadronically and one decays leptonically. The ‘Pre-selection’ consists of basic requirements, including that at least one tau-had-vis candidate and one lepton pass loose kinematic requirements. The ‘Object selections’ require exactly one tau-had-vis candidate, and at least two jets with pT > 25 GeV and abs(eta) < 2.5. The ‘Trigger specific offline pT cuts’ are cuts placed on the pT of the reconstructed jet or tau-had-vis that are geometrically matched to the HLT objects, to ensure the efficiencies of the HLT objects reach the plateau region.

Post-fit expected number of signal and background events and observed number of data events after applying the selection criteria and requiring exactly 2 b-tagged jets and assuming a background-only hypothesis.

Observed and expected limits at 95% CL on the cross-section of HH production, for the non-resonant ggF+VBF HH search, and the resonant HH search for four values of the resonance mass mX.

Acceptance times efficiency for the full analysis selections as a function of the resonance mass mX in the hadhad, lephad SLT and lephad LTT trigger categories, and the combined lephad. The acceptance times efficiency is evaluated for HH to bbtautau decays, with respect to the targeted tau-lepton decay modes (lephad or hadhad).

Post-fit distribution of mHH in the hadhad channel.

Post-fit distribution of mHH in the lephad SLT channel.

Post-fit distribution of mHH in the lephad LTT channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of mtautau MMC in the hadhad channel.

Post-fit distribution of of di-b-jet mass in the hadhad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

Post-fit distribution of di-b-jet mass in the LTT lephad channel.

BDT for SM HH in the hadhad channel

NN for SM HH in the lephad SLT channel

NN for SM HH in the lephad LTT channel

PNN for mX = 500 GeV resonant HH in the hadhad channel

PNN for mX = 500 GeV resonant HH in the lephad SLT channel

PNN for mX = 500 GeV resonant HH in the lephad LTT channel

PNN for mX = 1000 GeV resonant HH in the hadhad channel

PNN for mX = 1000 GeV resonant HH in the lephad SLT channel

PNN for mX = 1000 GeV resonant HH in the lephad LTT channel

Event yields as a function of log10(S/B) for data, background and non-resonant HH signal. Final discriminant bins from the hadhad, lephad SLT and lephad LTT categories are combined into bins of log10(S/B). The B is the fitted background yield assuming background-only hypothesis, and the signal S is scaled to the SM expected cross-section.

Observed and expected limits at 95% CL on the cross-section of the resonant HH production as a function of the scalar resonance mass mX.

Post-fit distribution of delta-R between the taus in the hadhad channel.

Post-fit distribution of delta-R between the b-tagged jets in the hadhad channel.

Post-fit distribution of delta-R between the taus in the lephad SLT channel.

Post-fit distribution of delta-R between the b-tagged jets in the lephad SLT channel.

Post-fit distribution of delta-pT between the tau and lepton in the lephad SLT channel.

Post-fit distribution of pT of the subleading b-tagged jet in the lephad SLT channel.

Post-fit distribution of MTW in the lephad SLT channel.

Post-fit distribution of missing transverse momentum in the lephad SLT channel.

Post-fit distribution of missing transverse momentum centrality in the lephad SLT channel.

Post-fit distribution of delta-phi between the Higgs boson candidates in the lephad SLT channel.

Post-fit distribution of delta-pT between the tau and lepton in the lephad LTT channel.

Post-fit distribution of delta-R between the taus in the lephad LTT channel.

Post-fit distribution of delta-phi between the lepton and the missing transverse momentum in the lephad LTT channel.

Post-fit distribution of delta-phi between the Higgs boson candidates in the lephad LTT channel.

Post-fit distribution of the total transverse momentum s in the lephad LTT channel.

PNN for mX = 300 GeV resonant HH in the hadhad channel

PNN for mX = 300 GeV resonant HH in the lephad SLT channel

PNN for mX = 300 GeV resonant HH in the lephad LTT channel

PNN for mX = 1600 GeV resonant HH in the hadhad channel

PNN for mX = 1600 GeV resonant HH in the lephad SLT channel

PNN for mX = 1600 GeV resonant HH in the lephad LTT channel

Local p-value of the background-only hypothesis as a function of the resonance mass.

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of ${E}_{T}^{miss}$ after the fit of the multijet backgrounds, in the electron channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on ${E}_{T}^{miss}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the signal region, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $W$+jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 3-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Distribution of $m_{T}^{W}$ after the fit of the multijet backgrounds, in the muon channel, in the $t\bar{t}$ 4-jets VR, without applying the cut on $m_{T}^{W}$. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit. The last bin includes the overflow. The uncertainty band indicates the simulation's statistical uncertainty, the normalisation uncertainties for different processes ($40$ % for $W$+jets production, $30$ % for multijet background and $6$ % for top-quark processes) and the multijet background shape uncertainty in each bin, summed in quadrature. The lower panel of the figure shows the ratio of the data to the prediction.

Expected distributions of the MEM discriminant $P(S|X)$ in the SR, for the s-channel single-top signal, and for the $t\bar{t}$ and $W$+jets backgrounds, for MEM discriminant values larger than $2.0\times10^{-4}$. Each distribution is normalised to unity. The binning is the same as the optimised binning used in the signal extraction fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $W$+jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 3-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty band indicates the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the $t\bar{t}$ 4-jets VR. Simulated events are normalised to the expected number of events given the integrated luminosity, after applying the normalisation factors obtained in the multijet fit presented in Section 5 in the paper. The uncertainty bands indicate the simulation's statistical uncertainty and the normalisation uncertainties for the various processes in each bin, summed in quadrature. The ratio of the observed number to the predicted number of events in each bin is shown in the lower panel of the figure, with different vertical axis ranges. The binning is the same as the optimised binning used in the signal extraction fit described in Section 8 in the paper, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR before the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore not included. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$. The lower panel of the figure shows the ratio of the data to the prediction, with different vertical axis ranges. The uncertainty band indicates the total uncertainties and their correlations in each bin. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Distribution of the MEM discriminant $P(S|X)$ in the SR after the fit to data, for MEM discriminant values larger than $2.0\times10^{-4}$, after subtraction of all backgrounds. The fitted distribution for the simulation of the signal is shown together with the post-fit uncertainty in the backgrounds. The binning is the same as the optimised binning used in the fit, resulting in a non-linear horizontal scale.

Pre-fit and post-fit event yields in the SR, for MEM discriminant values larger than $2.0\times10^{-4}$. The central value of the event yield for each process is calculated by summing the values of the discriminant bin contents, using the nominal expected yield for the pre-fit value, and the best-fit estimate for the post-fit value. The error includes statistical and systematic uncertainties summed in quadrature. All sources of systematic uncertainties are included, taking into account correlations and anti-correlations in the post-fit case. The uncertainties in the $t\bar{t}$ and $W$+jets normalisation factors, as well as in the s-channel signal cross-section, are not defined pre-fit and therefore only included in the post-fit uncertainties.

Observed impact of the different sources of uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. The 'Systematic uncertainties' category combines all sources of systematic uncertainties. The statistical uncertainty is obtained by repeating the fit after having fixed all nuisance parameters, including the $t\bar{t}$ and $W$+jets normalisation factors. 'Total' gives the total uncertainty on the measurement.

Observed impact of the different sources of $t\bar{t}$ modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and 'ME/PS matching' to the matching of the ME to the parton shower.

Observed impact of the different sources of s-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of t-channel modelling uncertainty on the measured s-channel signal cross-section. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, as described in Section 7 in the paper.

Observed impact of the different sources of $tW$ modelling uncertainty on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root. 'PS & had.' refers to the parton shower and hadronisation model, and '$t\bar{t}$ overlap' to the algorithm removing the overlap between $tW$ and $t\bar{t}$ production at NLO, as described in Section 7 in the paper.

Observed impact of the different sources of PDF uncertainties on the measured s-channel signal cross-section, grouped by categories. The impact of each category is obtained by repeating the fit after having fixed the set of nuisance parameters corresponding to that category, subtracting the square of the resulting uncertainty from the square of the uncertainty found in the full fit, and calculating the square root.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the leading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the subleading-jet $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $p_{T}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the lepton $\eta$. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the ${E}_{T}^{miss}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Comparison between data and prediction after the fit to data in the signal region for the $m_{T}^{W}$. The last bin includes the overflow. The uncertainty band includes all uncertainties and their correlations. The lower panel of the figure shows the ratio of the data to the prediction.

Nuisance parameters ranked according to their post-fit impacts on the best-fit value of the ratio $\mu$ of the measured cross-section to the predicted cross-section. In the figure, only the 20 nuisance parameters with the largest post-fit impacts are shown. The empty (solid) blue rectangles illustrate the pre-fit (post-fit) impact on $\mu$, corresponding to the upper axis. The pre-fit (post-fit) impact of each nuisance parameter, $\Delta\mu$, is calculated as the difference in the fitted value of $\mu$ between the nominal fit and the fit when fixing the corresponding nuisance parameter to $\hat{\theta}\pm\Delta\theta$ ($\hat{\theta}\pm\Delta\hat{\theta}$), where $\hat{\theta}$ is the best-fit value of the nuisance parameter and $\Delta\theta$ ($\Delta\hat{\theta}$) is its pre-fit (post-fit) uncertainty. Several systematic uncertainties are split into different nuisance parameters, which are indicated by NP. JES (JER) indicates jet energy scale (resolution), and $\gamma$ indicates a nuisance parameter associated to the MC statistics in one of the 18 bins numbered from 0 to 17. The black points show the best-fit values of the nuisance parameters, with the error bars representing the post-fit uncertainties. Each nuisance parameter is shown wrt. its nominal value, $\theta_0$, and in units of its pre-fit uncertainty, except the free-floating normalisation factors of the $t\bar{t}$ and $W$+jets backgrounds, and the parameters associated to the MC statistics in each bin, for which the post-fit values and uncertainties are shown.

Correlation matrix of the nuisance parameters and of the ratio $\mu$ of the measured cross-section to the predicted cross-section. The correlations are given after the fit to data. In the figure, only the parameters which have a correlation of at least 0.2 with any other parameter are shown.

Distribution of the MEM discriminant $P(S|X)$ in the SR for MEM discriminant values larger than $2.0\times10^{-4}$, for the collision data used for the measurement, and for 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.

Measured values of the signal cross-section and of the $t\bar{t}$ and $W$+jets normalisation factors, obtained by statistical-only fits to the collision data used for the measurement, and to 1000 pseudo-data replicas, generated using a bootstrapping technique, in order to assess the statistical correlations between this measurement and others, for the purpose of combinations. The central values and their statistical uncertainties are obtained by repeating the fit after having fixed all nuisance parameters, except the $t\bar{t}$ and $W$+jets normalisation factors, which are let free-floating (unlike for the statistical uncertainty on the cross-section quoted in the paper). The replicas are obtained by reweighting each observed data event by a random integer generated according to Poisson statistics, using the <a href="https://zenodo.org/record/5361038">BootstrapGenerator</a> software package , which implements a technique described in <a href="https://cds.cern.ch/record/2759945/">ATL-PHYS-PUB-2021-011</a>. The ATLAS event number and run number of each event are used as seed to uniquely but reproducibly initialise the random number generator for each event. Each pseudo-data replica is assigned an index, ranging from 0 to 999, corresponding to the random number index used consistently for each observed data event.

Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$.

Signal region detector-level distribution for the observable $m_{e\mu}$.

Signal region detector-level distribution for the observable $p_{\mathrm{T}}^{e\mu}$.

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

Measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

Relative systematic uncertainties for the fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|y_{e\mu}|$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $|\Delta \phi(e \mu)|$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $ \cos\theta^{\ast}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{\mathrm{lead}\, \ell}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $m_{e\mu}$

The statistical correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

The total correlation coefficients (in percentage) between bins for the measured fiducial differential cross-section of $WW \rightarrow e^{\pm}\nu\mu^{\mp}\nu$ production for the observable $p_{\mathrm{T}}^{e\mu}$

Observed (points with error bars) and expected background (solid histograms) distributions for $E_{T}^{miss}$ in the signal region (a) SRL, (b) SRM and (c) SRH after the background-only fit applied to the CRs. The predicted signal distributions for the two models with a gluino mass of 2000 GeV and neutralino mass of 250 GeV (SRL), 1050 GeV (SRM) or 1950 GeV (SRH) are also shown for comparison. The uncertainties in the SM background are only statistical.

Observed and expected exclusion limit in the gluino-neutralino mass plane at 95% CL combined using the signal region with the best expected sensitivity at each point, for the full Run-2 dataset corresponding to an integrated luminosity of $139~\mathrm{fb}^{-1}$, for $\gamma/Z$ (a) and $\gamma/h$ (b) signal models. The black solid line corresponds to the expected limits at 95% CL, with the light (yellow) bands indicating the 1$\sigma$ exclusions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves, the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties. For each point in the higgsino-bino parameter space, the labels indicate the best-expected signal region, where L, M and H mean SRL, SRM and SRH, respectively.

Observed and expected exclusion limit in the gluino-neutralino mass plane at 95% CL combined using the signal region with the best expected sensitivity at each point, for the full Run-2 dataset corresponding to an integrated luminosity of $139~\mathrm{fb}^{-1}$, for $\gamma/Z$ (a) and $\gamma/h$ (b) signal models. The black solid line corresponds to the expected limits at 95% CL, with the light (yellow) bands indicating the 1$\sigma$ exclusions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves, the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties. For each point in the higgsino-bino parameter space, the labels indicate the best-expected signal region, where L, M and H mean SRL, SRM and SRH, respectively.

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/Z$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Acceptance (left) and efficiency (right) for the $\gamma/h$ model signal grid for SRL (top), SRM (middle) and SRH (bottom).

Cutflow for the SRL selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 250 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.

Cutflow for the SRM selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 1050 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.

Cutflow for the SRH selection, for two relevant signal points for both $\gamma/Z$ and $\gamma/h$ models, where the gluinos have mass of 2000 GeV and the neutralinos have a mass of 1950 GeV (10000 generated events). The numbers are normalized to a luminosity of 139 $fb^{-1}$.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

Observed and expected exclusion limits in the gluino–neutralino mass plane at 95% CL for the full Run-2 dataset corresponding to an integrated luminosity of 139 fb−1 , for the (a) $\gamma/Z$ and (b) $\gamma/h$ signal models. They are obtained by combining limits from the signal region with the best expected sensitivity at each point. The dashed (black) line corresponds to the expected limits at 95% CL, with the light (yellow) band indicating the $\pm 1\sigma$ excursions due to experimental and background-theory uncertainties. The observed limits are indicated by medium (red) curves: the solid contour represents the nominal limit, and the dotted lines are obtained by varying the signal cross section by the theoretical scale and PDF uncertainties.

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.

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.