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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Distribution of $m_{\mathrm{JJ}}$ in the SR (black points) compared to the background-only expectation, where the shape is derived from the CR and the normalisation is fitted in the SR.

Observed and expected limits at 95% CL on the cross-section times branching ratio for the production of dark quarks through a $Z^\prime$ as a function of the $Z^\prime$ mass for model A (see the text for details). The darker and lighter shaded bands around the expected limits represent the $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty range, respectively.

The predicted theory cross-section times branching ratio for the production of dark quarks through a $Z^\prime$ as a function of the $Z^\prime$ mass for model A (see the text for details). In the theory model, a universal coupling of the $Z^\prime$ to each SM quark $g_q$ is assumed and the dark quarks are all represented by one species with an effective coupling $g_{q_d}$ to the $Z^\prime$. For model A, the predicted cross-section is shown for $g_q=0.05$ and $g_{q_d} = 0.2$.

Observed and expected limits at 95% CL on the cross-section times branching ratio for the production of dark quarks through a $Z^\prime$ as a function of the $Z^\prime$ mass for model B (see the text for details). The darker and lighter shaded bands around the expected limits represent the $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty range, respectively.

The predicted theory cross-section times branching ratio for the production of dark quarks through a $Z^\prime$ as a function of the $Z^\prime$ mass for model B (see the text for details). In the theory model, a universal coupling of the $Z^\prime$ to each SM quark $g_q$ is assumed and the dark quarks are all represented by one species with an effective coupling $g_{q_d}$ to the $Z^\prime$. For model B, the predicted cross-section is shown for $g_q=0.15$ and $g_{q_d} = 0.5$.

Observed and expected limits at 95% CL on the cross-section times branching ratio for the production of dark quarks through a $Z^\prime$ as a function of the $Z^\prime$ mass for model C (see the text for details). The darker and lighter shaded bands around the expected limits represent the $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty range, respectively.

The predicted theory cross-section times branching ratio for the production of dark quarks through a $Z^\prime$ as a function of the $Z^\prime$ mass for model C (see the text for details). In the theory model, a universal coupling of the $Z^\prime$ to each SM quark $g_q$ is assumed and the dark quarks are all represented by one species with an effective coupling $g_{q_d}$ to the $Z^\prime$. For model C, the predicted cross-section is shown for $g_q=0.15$ and $g_{q_d}= 0.5$.

Observed and expected limits at 95% CL on the cross-section times branching ratio for the production of dark quarks through a $Z^\prime$ as a function of the $Z^\prime$ mass for model D (see the text for details). The darker and lighter shaded bands around the expected limits represent the $\pm 1 \sigma$ and $\pm 2 \sigma$ uncertainty range, respectively.

The predicted theory cross-section times branching ratio for the production of dark quarks through a $Z^\prime$ as a function of the $Z^\prime$ mass for model D (see the text for details). In the theory model, a universal coupling of the $Z^\prime$ to each SM quark $g_q$ is assumed and the dark quarks are all represented by one species with an effective coupling $g_{q_d}$ to the $Z^\prime$. For model D, the predicted cross-section is shown for $g_q=0.05$ and $g_{q_d} = 0.2$.

Relative efficiency (in %) of each stage of the analysis selection with respect to the previous one for models A through D and $m_{Z^\prime}=2.5$ TeV. Approximately 90000 events were generated per signal mass hypothesis.

Summary of results for cross-section of non-prompt $\psi(2S)$ decaying to a muon pair for 13 TeV data in fb/GeV. Uncertainties are statistical and systematic, respectively.

Summary of results for non-prompt fraction of $J/\psi$ decaying to a muon pair as a percentage for 13 TeV data. Uncertainties are statistical and systematic, respectively.

Summary of results for non-prompt fraction of $\psi(2S)$ decaying to a muon pair as a percentage for 13 TeV data. Uncertainties are statistical and systematic, respectively.

Summary of results for prompt $\psi(2S)$ over $J/\psi$ ratio as a percentage for 13 TeV data. Uncertainties are statistical and systematic, respectively.

Summary of results for non-prompt $\psi(2S)$ over $J/\psi$ ratio as a percentage for 13 TeV data. Uncertainties are statistical and systematic, respectively.

Correction factors for an assumed angular dependence $\propto 1+\lambda_{\theta} \cos^2\theta $ in the helicity frame, for several values of the parameter $\lambda_{\theta}$. The correction factors were found to be the same (within 1% - 2%) for $J\psi$ and $\psi(2S)$ mesons, for prompt and non-prompt production mechanisms, and for the three rapidity intervals considered in this paper.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.16 < $\alpha$ < 0.18.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.18 < $\alpha$ < 0.20.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.20 < $\alpha$ < 0.22.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.22 < $\alpha$ < 0.24.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.24 < $\alpha$ < 0.26.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.26 < $\alpha$ < 0.28.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.28 < $\alpha$ < 0.30.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.30 < $\alpha$ < 0.32.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.32 < $\alpha$ < 0.34.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.10 < $\alpha$ < 0.12.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.12 < $\alpha$ < 0.14.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.14 < $\alpha$ < 0.16.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.16 < $\alpha$ < 0.18.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.18 < $\alpha$ < 0.20.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.20 < $\alpha$ < 0.22.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.22 < $\alpha$ < 0.24.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.24 < $\alpha$ < 0.26.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.26 < $\alpha$ < 0.28.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.28 < $\alpha$ < 0.30.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.30 < $\alpha$ < 0.32.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The product of the analysis acceptance and selection efficiency for all analysis selection criteria is shown as a function of $m_Y$ and $m_{X}/m_{Y}$.

The cross-section of the signal samples is shown as a function of $m_Y$ and $m_{X}/m_{Y}$.

Normalisation factors for the major background processes and ttW ($\Delta \eta < 0$) from the fit using only statistical uncertainties (fixing Nuisance Parameters (NP) to their fitted values) to data in all analysis regions.

Normalisation factors for the major background processes and ttW ($\Delta \eta < 0$) from the fit using using both statistical and systematic uncertainties to data in all analysis regions.

Correlation matrix between the Normalisation Factors in the fit using only statistical uncertainties (fixing Nuisance Parameters (NP) to their fitted values) to data in all analysis regions.

The most relevant systematic uncertainties ranked by their impact on the leptonic charge asymmetry ($A_c^{\ell}$) parameter at reconstructed level. The impact of the uncertainties is shown before and after the combined profile-likelihood fit to data in the signal and control regions. Pulls introduced by the fitting procedure are also shown. The entries shown in bold are the uncertainties of the freely floating background normalisations. ME stands for "matrix element", PS for "parton shower" and JER for "jet energy resolution". The gamma-uncertainties refer to the MC statistical uncertainties in a specific region and bin.

The most relevant systematic uncertainties ranked by their impact on the ttW Normalisation Factor ($\Delta \eta < 0$) parameter at reconstructed level. The impact of the uncertainties is shown before and after the combined profile-likelihood fit to data in the signal and control regions. Pulls introduced by the fitting procedure are also shown. ME stands for "matrix element", PS for "parton shower" and JER for "jet energy resolution". The gamma-uncertainties refer to the MC statistical uncertainties in a specific region and bin.

The predicted and observed numbers of events in the control and signal regions. The predictions are shown before the fit to data. The indicated uncertainties consider statistical as well as all experimental and theoretical systematic uncertainties. Background categories with event yields with a value of 1.0e-6 have a negligible contribution to the region and are manually set to that value.

The predicted and observed numbers of events in the control and signal regions. The predictions are shown after the fit to data. The indicated uncertainties consider statistical as well as all experimental and theoretical systematic uncertainties. Background categories with event yields with a value of 1.0e-6 have a negligible contribution to the region and are manually set to that value.