Muon reconstruction efficiency as function of its tranverse impact parameter, $d_0$.

Electron selection efficiency as function of its tranverse momentum, $p_T$.

Muon selection efficiency as function of its tranverse momentum, $p_T$.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the 1 b jet, 0 forward jet, $t\leq 0$ signal region in the single lepton channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the 1 b jet, 0 forward jet, $t>0$ signal region in the single lepton channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the 1 b jet, $\geq 1$ forward jet, $t\leq 0$ signal region in the single lepton channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the 1 b jet, $\geq 1$ forward jet, $t>0$ signal region in the single lepton channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the $\geq 2$ b jet, $t\leq 0$ signal region in the single lepton channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the $\geq 2$ b jet, $t>0$ signal region in the single lepton channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The post-fit NN output score distribution of the $\geq 2$ b jet, different flavor signal region in the dileptonic channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The post-fit NN output score distribution of the $\geq 2$ b jet, same flavor signal region in the dileptonic channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The post-fit NN output score distribution of the 1 b jet, different flavor signal region in the dileptonic channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The post-fit NN output score distribution of the 1 b jet, same flavor signal region in the dileptonic channel. A representative signal model distribution is shown for the scalar mediator interaction with $(m_{\chi},m_{\phi})=(1,100)$GeV and couplings set to unity. The grey dashed area in the upper panel represents the total uncertainty in all of the backgrounds and the chosen signal model, while in the lower panel it represents only the total uncertainty in the backgrounds.

The model-independent 95\% CL limits on production cross section for new physics processes for the scalar interaction. The expected limit is shown by the black dashed line with the 68 and 95\% CL uncertainty bands in green and yellow, respectively, while the observed limit is shown by the solid black line. Theoretical LO cross section values for the DM model and their associated uncertainties are also presented (grey line).

The model-independent 95\% CL limits on production cross section for new physics processes for the pseudoscalar interaction. The expected limit is shown by the black dashed line with the 68 and 95\% CL uncertainty bands in green and yellow, respectively, while the observed limit is shown by the solid black line. Theoretical LO cross section values for the DM model and their associated uncertainties are also presented (grey line).

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the QCD control region in the all hadronic channel. The grey dashed area in the upper and lower panels represents the total uncertainty in all of the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the tt(1l) control region in the all hadronic channel. The grey dashed area in the upper and lower panels represents the total uncertainty in all of the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the W+jets control region in the all hadronic channel. The grey dashed area in the upper and lower panels represents the total uncertainty in all of the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the Z(2l) control region in the all hadronic channel. The grey dashed area in the upper and lower panels represents the total uncertainty in all of the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the W+jets control region in the single lepton channel. The grey dashed area in the upper and lower panels represents the total uncertainty in all of the backgrounds.

The post-fit $p_{\mathrm{T}}^{\text{miss}}$ distribution of the tt(2l) control region in the single lepton channel. The grey dashed area in the upper and lower panels represents the total uncertainty in all of the backgrounds.

The post-fit $(N_{\text{jet}},N_{\text{bjet}})$ distribution of the ttZ control region in the dileptonic channel. The grey dashed area in the upper and lower panels represents the total uncertainty in all of the backgrounds.

The post-fit NN distribution of the Drell-Yan control region in the dileptonic channel. The grey dashed area in the upper and lower panels represents the total uncertainty in all of the backgrounds.

Predicted signal yields for the 2016, 2017, and 2018 data-taking periods, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{\phi}) = (1,50)$ GeV scalar mediator signal hypothesis for the all hadronic channel. The requirements listed up to and including the $M_{T}^{b}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last three requirements show the separate yields after the cumulative requirements up to the $M_{T}^{b}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{\phi}) = (1,500)$ GeV scalar mediator signal hypothesis for the all hadronic channel. The requirements listed up to and including the $M_{T}^{b}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last three requirements show the separate yields after the cumulative requirements up to the $M_{T}^{b}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{a}) = (1,50)$ GeV pseudoscalar mediator signal hypothesis for the all hadronic channel. The requirements listed up to and including the $M_{T}^{b}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last three requirements show the separate yields after the cumulative requirements up to the $M_{T}^{b}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{a}) = (1,500)$ GeV pseudoscalar mediator signal hypothesis for the all hadronic channel. The requirements listed up to and including the $M_{T}^{b}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last three requirements show the separate yields after the cumulative requirements up to the $M_{T}^{b}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{\phi}) = (1,50)$ GeV scalar mediator signal hypothesis for the all hadronic channel. The requirements listed up to and including the $M_{T}^{b}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last three requirements show the separate yields after the cumulative requirements up to the $M_{T}^{b}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{\phi}) = (1,500)$ GeV scalar mediator signal hypothesis for the all hadronic channel. The requirements listed up to and including the $M_{T}^{b}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last three requirements show the separate yields after the cumulative requirements up to the $M_{T}^{b}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{a}) = (1,50)$ GeV pseudoscalar mediator signal hypothesis for the all hadronic channel. The requirements listed up to and including the $M_{T}^{b}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last three requirements show the separate yields after the cumulative requirements up to the $M_{T}^{b}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{a}) = (1,500)$ GeV pseudoscalar mediator signal hypothesis for the all hadronic channel. The requirements listed up to and including the $M_{T}^{b}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last three requirements show the separate yields after the cumulative requirements up to the $M_{T}^{b}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{\phi}) = (1,50)$ GeV scalar mediator signal hypothesis for the single lepton channel. The requirements listed up to and including the $M_{T2}^{W}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last six requirements show the separate yields after the cumulative requirements up to the $M_{T2}^{W}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{\phi}) = (1,500)$ GeV scalar mediator signal hypothesis for the single lepton channel. The requirements listed up to and including the $M_{T2}^{W}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last six requirements show the separate yields after the cumulative requirements up to the $M_{T2}^{W}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{a}) = (1,50)$ GeV pseudoscalar mediator signal hypothesis for the single lepton channel. The requirements listed up to and including the $M_{T2}^{W}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last six requirements show the separate yields after the cumulative requirements up to the $M_{T2}^{W}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{a}) = (1,500)$ GeV pseudoscalar mediator signal hypothesis for the single lepton channel. The requirements listed up to and including the $M_{T2}^{W}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last six requirements show the separate yields after the cumulative requirements up to the $M_{T2}^{W}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{\phi}) = (1,50)$ GeV scalar mediator signal hypothesis for the single lepton channel. The requirements listed up to and including the $M_{T2}^{W}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last six requirements show the separate yields after the cumulative requirements up to the $M_{T2}^{W}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{\phi}) = (1,500)$ GeV scalar mediator signal hypothesis for the single lepton channel. The requirements listed up to and including the $M_{T2}^{W}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last six requirements show the separate yields after the cumulative requirements up to the $M_{T2}^{W}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{a}) = (1,50)$ GeV pseudoscalar mediator signal hypothesis for the single lepton channel. The requirements listed up to and including the $M_{T2}^{W}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last six requirements show the separate yields after the cumulative requirements up to the $M_{T2}^{W}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{a}) = (1,500)$ GeV pseudoscalar mediator signal hypothesis for the single lepton channel. The requirements listed up to and including the $M_{T2}^{W}$ cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last six requirements show the separate yields after the cumulative requirements up to the $M_{T2}^{W}$ cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{\phi}) = (1,50)$ GeV scalar mediator signal hypothesis for the dileptonic channel. The requirements listed up to and including the Z window cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last four requirements show the separate yields after the cumulative requirements up to the Z window cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{\phi}) = (1,500)$ GeV scalar mediator signal hypothesis for the dileptonic channel. The requirements listed up to and including the Z window cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last four requirements show the separate yields after the cumulative requirements up to the Z window cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{a}) = (1,50)$ GeV pseudoscalar mediator signal hypothesis for the dileptonic channel. The requirements listed up to and including the Z window cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last four requirements show the separate yields after the cumulative requirements up to the Z window cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the t+DM $(m_{\chi},m_{a}) = (1,500)$ GeV pseudoscalar mediator signal hypothesis for the dileptonic channel. The requirements listed up to and including the Z window cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last four requirements show the separate yields after the cumulative requirements up to the Z window cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{\phi}) = (1,50)$ GeV scalar mediator signal hypothesis for the dileptonic channel. The requirements listed up to and including the Z window cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last four requirements show the separate yields after the cumulative requirements up to the Z window cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{\phi}) = (1,500)$ GeV scalar mediator signal hypothesis for the dileptonic channel. The requirements listed up to and including the Z window cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last four requirements show the separate yields after the cumulative requirements up to the Z window cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{a}) = (1,50)$ GeV pseudoscalar mediator signal hypothesis for the dileptonic channel. The requirements listed up to and including the Z window cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last four requirements show the separate yields after the cumulative requirements up to the Z window cut.

Predicted signal yields for the 2016, 2017, and 2018 data-taking period, corresponding to $36.3\,\text{fb}^{-1}$, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, $41.5\,\text{fb}^{-1}$, and $59.7\,\text{fb}^{-1}$, respectively, after the application of each search requirement for the tt+DM $(m_{\chi},m_{a}) = (1,500)$ GeV pseudoscalar mediator signal hypothesis for the dileptonic channel. The requirements listed up to and including the Z window cut are cumulative such that only the events and objects passing a given requirement are considered in subsequent requirements. The last four requirements show the separate yields after the cumulative requirements up to the Z window cut.

Cross sections calculated to leading order (LO) accuracy for the tt+DM scalar mediator signal process considering all possible decays from the visible particles in the final state.

Cross sections calculated to leading order (LO) accuracy for the tt+DM pseudoscalar mediator signal process considering all possible decays from the visible particles in the final state.

Cross sections calculated to leading order (LO) accuracy for the tt+DM scalar mediator signal process considering dileptonic decays from the visible particles in the final state.

Cross sections calculated to leading order (LO) accuracy for the tt+DM pseudoscalar mediator signal process considering dileptonic decays from the visible particles in the final state.

Cross sections calculated to leading order (LO) accuracy for the t+DM t-channel scalar mediator signal process considering all possible decays from the visible particles in the final state.

Cross sections calculated to leading order (LO) accuracy for the t+DM tW-channel scalar mediator signal process considering all possible decays from the visible particles in the final state.

Cross sections calculated to leading order (LO) accuracy for the t+DM t-channel pseudoscalar mediator signal process considering all possible decays from the visible particles in the final state.

Cross sections calculated to leading order (LO) accuracy for the t+DM tW-channel pseudoscalar mediator signal process considering all possible decays from the visible particles in the final state.

Cross sections calculated to leading order (LO) accuracy for the t+DM tW-channel scalar mediator signal process considering dileptonic decays from the visible particles in the final state.

Cross sections calculated to leading order (LO) accuracy for the t+DM tW-channel pseudoscalar mediator signal process considering dileptonic decays from the visible particles in the final state.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.5 < |y| < 2.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.4$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $|y| < 0.5$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $0.5 < |y| < 1.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.0 < |y| < 1.5$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ measured in $1.5 < |y| < 2.0$ for jets clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $|y| < 0.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $0.5 < |y| < 1.0$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.0 < |y| < 1.5$and $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $|y| < 0.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $0.5 < |y| < 1.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$and $1.0 < |y| < 1.5$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.

The correlation matrix at the particle level of inclusive jet production cross section as a function of the jet transverse momentum~$p_\mathrm{T}$ for jets in $1.5 < |y| < 2.0$ clustered using the anti-$k_\mathrm{t}$ algorithm with $R=0.7$. It contains contributions from the data and from the \textsc{PYTHIA}~8 sample used to perform the unfolding.