The observed 95% CL exclusion limits as a function of κ_2V (obtained using the signal strength μ_VBF as the POI) from the combined ggF and VBF signal regions, as shown by the solid black line. The value of κ_λ is fixed to 1. The blue and yellow bands show respectively the 1σ and 2σ bands around the expected exclusion limits, which are shown by the dashed black line. The expected exclusion limits are obtained using a fit to the data with the background-only hypothesis. The dark red line shows in the predicted VBF HH cross-section as a function of κ_2V.

The observed 95% CL exclusion limit obtained using the signal strength μ_ggF+VBF as the POI in the two-dimensional κ_λ vs. κ_2V space, obtained from the combined ggF and VBF signal model, as shown by the solid black line. The blue and yellow bands show respectively the 1σ and 2σ bands around the expected exclusion limits, which are shown by the dashed black line. The star denotes the SM prediction (κ_λ=κ_2V=1). The displayed values are in units of $\mu_{ggF}$.

The expected and observed profile likelihood ratio scans for κ_λ coupling modifier, shown by the solid black line, using the coupling modifier as the POIs. The value of κ_2V is fixed to 1. The dashed blue line shows the expected profile likelihood ratio, as obtained using a fit to the data with the background-only hypothesis. The pink line indicates the 2σ exclusion boundary.

The expected and observed profile likelihood ratio scans for the κ_2V coupling modifier, shown by the solid black line, using the coupling modifier as the POIs. The value of κ_λ is fixed to 1. The dashed blue line shows the expected profile likelihood ratio, as obtained using a fit to the data with the background-only hypothesis. The pink line indicates the 2σ exclusion boundary.

The observed profile likelihood ratio exclusion limits for the two-dimensional κ_λ vs. κ_2V modifier space, shown by the solid dark purple line at the 1σ level and the dashed turquoise line at the 2σ level. The black cross denotes the best-fit values of (κ_λ,κ_2V). For both the expected and observed limit plots, the black star indicates the SM prediction (κ_λ=κ_2V=1).

The expected profile likelihood ratio exclusion limits for the two-dimensional κ_λ vs. κ_2V modifier space, where the solid pink line denotes the 1σ-level exclusion and the dashed orange line denotes the 2σ-level exclusion. The black cross denotes the best-fit values of (κ_λ,κ_2V). For both the expected and observed limit plots, the black star indicates the SM prediction (κ_λ=κ_2V=1).

The observed 95% CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_{H G} vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95% CL exclusion limits. The shaded blue band indicates the ± 1σ uncertainty of the exclusion limits, while the yellow band indicates the ± 2σ uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

The observed 95% CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_tG vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95% CL exclusion limits. The shaded blue band indicates the ± 1σ uncertainty of the exclusion limits, while the yellow band indicates the ± 2σ uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

The observed 95% CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_{t H} vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95% CL exclusion limits. The shaded blue band indicates the ± 1σ uncertainty of the exclusion limits, while the yellow band indicates the ± 2σ uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

The observed 95% CL exclusion limits on the SMEFT coefficients in the two-dimensional spaces c_H□ vs. c_H shown by the solid black lines. The displayed values are in units of $\mu_{ggF}$. The dashed black line indicates the expected 95% CL exclusion limits. The shaded blue band indicates the ± 1σ uncertainty of the exclusion limits, while the yellow band indicates the ± 2σ uncertainty. The values of the other three coefficients for each plot are fixed to 0. The VBF HH process is ignored for this result.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2016 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2017 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

Distributions of the reconstructed m_HH in data (shown by the black points) and the estimated background (shown by the yellow histograms), in each of the six |Δη_HH|/X_HH categories in the ggF signal region, for the 2018 data. The hatching shows the total uncertainty on the background estimate. The distribution of the expected background is obtained using the best-fit values of the nuisance parameters in the background-only fit to the data. Distributions of the SM and κ_λ= 6 signal models are overlaid, scaled so as to be visible on the plot, and the scaling for each signal model is the same across the six categories. The lower panels show the ratio of the observed data yield to the predicted background in each bin. Events in the underflow and overflow bins are counted in the yields of the initial and final bins respectively. In the HEPData entry, the raw value per histogram bin is provided, while in the published paper the values in the histogram are scaled by the bin width.

The observed 95% CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H□, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1σ and 2σ bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95% CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H□, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1σ and 2σ bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95% CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H□, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1σ and 2σ bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95% CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H□, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1σ and 2σ bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95% CL exclusion limits as a function of the five relevant SMEFT coefficients: (a) c_H, (b) c_{H G}, (c) c_H□, (d) c_tH, and (e) c_tG, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1σ and 2σ bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95% CL exclusion limits as a function of the two relevant HH HEFT coefficients: (a) c_tt̄HH and (b) c_ggHH, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1σ and 2σ bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

The observed 95% CL exclusion limits as a function of the two relevant HH HEFT coefficients: (a) c_tt̄HH and (b) c_ggHH, obtained from the combined ggF signal model, as shown by the solid black line. The values of the other four coefficients are fixed to 0. The blue and yellow bands show the 1σ and 2σ bands respectively on the expected exclusion limits, which are shown by the dashed black line. The red line shows the predicted ggF HH cross-section as a function of the coefficient. The VBF HH process is ignored for this result.

Correlation matrix of all the parameters including nuisance parameters used in the profile likelihood unfolding fit on $A_{C}$.

Measured differential cross section as a function of observable $ \Delta R (l,l)$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ N_{jets}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet1}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet2}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{Jet2}/p_{T}^{Jet1}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ m_{jj}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ m_{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ H_{T}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{\gamma} / \sqrt{H_{T}}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \Delta \phi (Jet,\gamma)$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \phi_{CS}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ \cos \theta_{CS}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma} / m_{ll\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll} - p_{T}^{\gamma}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Measured differential cross section as a function of observable $ p_{T}^{ll\gamma j}$. Error on the measured cross-section include all the systematic uncertainties. SM predictions are produced with the event generators at particle level: Sherpa 2.2.4, Sherpa 2.2.11, MadGraph5_aMC@NLO, and MiNNLO$_{PS}$. Fixed order calculations results use MATRIX NNLO. Error represent statistical uncertainty and theoretical uncertainty (PDF and Scale variations).

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{\gamma} / \sqrt{H_{T}}$ (Fig. 8 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ H_{T}$ (Fig. 8 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \Delta \phi (Jet,\gamma)$ (Fig. 8 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \Delta R (l,l)$ (Fig. 5 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma}$ (Fig. 5 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } p_{T}^{ll} + p_{T}^{\gamma} < 200 GeV$ (Fig. 11 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } 200 GeV < p_{T}^{ll} + p_{T}^{\gamma} < 300 GeV$ (Fig. 11 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} - p_{T}^{\gamma} \textrm{ in bin } p_{T}^{ll} + p_{T}^{\gamma} > 300 GeV$ (Fig. 11 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ m_{jj}$ (Fig. 7 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ m_{ll\gamma j}$ (Fig. 7 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ N_{jets}$ (Fig. 6 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet1}$ (Fig. 6 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet2}$ (Fig. 6 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{Jet2}/p_{T}^{Jet1}$ (Fig. 6 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll}$ (Fig. 5 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j}$ (Fig. 8 (d))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } p_{T}^{ll\gamma} < 50 GeV$ (Fig. 12 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } 50 GeV < p_{T}^{ll\gamma} < 75 GeV$ (Fig. 12 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma j} \textrm{ in bin } p_{T}^{ll\gamma} > 75 GeV$ (Fig. 12 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } 125 GeV < m_{ll\gamma} < 200 GeV$ (Fig. 10 (a))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } 200 GeV < m_{ll\gamma} < 300 GeV$ (Fig. 10 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll\gamma} / m_{ll\gamma} \textrm{ in bin } m_{ll\gamma} > 300 GeV$ (Fig. 10 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ p_{T}^{ll} + p_{T}^{\gamma}$ (Fig. 5 (c))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \cos \theta_{CS}$ (Fig. 9 (b))

Correlation matrix of the statistical uncertainties in the measured fiducial cross section for the observable $ \phi_{CS}$ (Fig. 9 (a))

Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($\tilde{G}$) and two $Z$ bosons (left), a $Z$ and a Higgs boson ($H$) (center), or two Higgs bosons (right).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $\Delta R$ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower).

TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events.

TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w.~p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events.

The efficiency of the TD jet tagger working point used in the analysis is shown as a function of the lab frame transverse decay length for simulated signals with $\chi$ mass of 400 GeV. The uncertainties shown account for lifetime dependence and statistical uncertainty.

The TD jet tagger score distributions for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. The last bin contains jets with tagger scores greater than 0.996, the threshold used to tag signal jets. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.

The TD jet tagger misidentification probability measured using the nominal $W$+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well ($Z$+jets MR as green round markers, $t\bar{t}$ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.

The TD jet tagger misidentification probability measured using the nominal $W$+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well ($Z$+jets MR as green round markers, $t\bar{t}$ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. On the left, this probability is shown for the first 19.9 fb$^{-1}$ of data collected in 2016, while on the right it is shown for the last 16.4 fb$^{-1}$ of data collected in 2016combined with data collected in 2017-2018.

Distribution of the number of TD tagged jets for the $m_{\chi} = 400$ GeVsimulated signal samples with $c\tau_{\chi} = 0.5$ m (solid red line) and $c\tau_{\chi} = 3.0$ m (dotted green line), estimated background (blue square markers), and data (black round markers). The signal distributions are normalized to the expected cross section limit. The blue shaded region indicates the systematic uncertainty in the background prediction. No background prediction is shown for the bin with zero TD tagged jets as it is the main control region used to predict the background for the other two bins. There are zero observed events in the bin with two or more TD tagged jets.

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $m_\chi$ in a scenario with $\mathcal{B}(\chi\to HG) = 0.5$ and $c\tau = 0.5$ m (left) or 3 m (right).

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).

Expected and observed 95% CL upper limits on $\sigma_{\chi\chi}$ as functions of $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$ and $m_{\chi} = 400$ GeV (left) or 1000 GeV (right).

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.5$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The distribution of the jet charged hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.

The distribution of the jet neutral hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.

The distribution of the number of track constituents in the jet, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $W\to e\nu_e$ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $Z\to\ell^+\ell^-\gamma$ sample.

The $\eta$ distribution of TD-tagged jets in a background-enriched control region that comprises events with exactly one TD-tagged jet. Observed data (black round markers) and the corresponding prediction based on control samples in data (empty squared markers), measured using the nominal $W$+jets MR, are compared. The prediction uncertainty (gray band) includes the systematic uncertainty quantifying the degree of process dependence measured from alternative MRs. The predictions for the shape and the normalization of the $\eta$ distribution are consistent with the data.

Jet time distribution in a sample of b-tagged jets from dilepton $t \bar{t}$ events in 2017 data-taking period (black round markers) and simulation (filled histogram). A Gaussian smearing procedure is applied to the jet time in the $t \bar{t}$ sample (green line) to correct for effects that are difficult to simulate (timing drift caused by crystals transparency loss due to detector aging, electronics jitter).

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 1$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.75$, $\mathcal{B}(\chi\to Z\tilde{G}) = 0.25$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to H\tilde{G}) = 0.25$, $\mathcal{B}(\chi\to Z\tilde{G}) = 0.75$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The observed 95% CL upper limit on $\sigma_{\chi\chi}$ as a function of $m_{\chi}$ and $c\tau_{\chi}$ in a scenario with $\mathcal{B}(\chi\to Z\tilde{G}) = 1$. The area enclosed by the dotted black line corresponds to the observed excluded region.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 127 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 150 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 175 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 200 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 300 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 400 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 600 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1000 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1250 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1500 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the merged topology, namely, the H (or Z) decay products are produced with an angular separation $\Delta R < 0.8$, and the H (or Z) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 3\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with exactly one quark in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and only one b-quark (or light quark) has $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 5\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

The efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The sample used to compute the efficiency contains events with pair production of $\chi$ with a mass of 1800 GeV and a lifetime of 0.5 and 3 m, and considering the combinations of the $\chi$ decay modes considered in this search ($H \tilde{G} \rightarrow b\bar{b} \tilde{G}$ or $Z\tilde{G} \rightarrow q\bar{q} \tilde{G}$). The efficiency is calculated on top of the acceptance definition for the resolved topology with two quarks in acceptance, namely, the H (or Z) decay products are produced with an angular separation $\Delta R \geq 0.8$, and both b-quarks (or light quarks) have $p_T > 30$ GeV and $|\eta|<1$. The full simulation signal yield prediction can be reproduced within 7\%. This nonclosure uncertainty is added in quadrature to the statistical uncertainty of each bin.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 127 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 127 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 150 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 150 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 175 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 175 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 200 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 200 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 250 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 250 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 300 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 300 GeV.

Cutflow table for a $\tilde{\chi}_{1}^{0}$ signal sample with a mass of 400 GeV.

Expected 2-D likelihood scan of the signal strength $\mu_{\tau\tau}$ versus the $CP$-mixing angle $\phi_{\tau}$.

Free-floating parameters in the measurement.

Expected and observed upper limits at 95% CL on the cross section of vector-like quark pair production for $B\bar{B}$ in the singlet model.

Expected and observed upper limits at 95% CL on the cross section of vector-like quark pair production for $T\bar{T}$ in the doublet model.

Expected and observed upper limits at 95% CL on the cross section of vector-like quark pair production, considereing both $T\bar{T}$ and $B\bar{B}$, in the $(T,B)$ doublet model.

Expected 95% CL lower limits on the vector-like top quark mass as a function of the branching ratios into $Wb$ and $Ht$.

Observed 95% CL lower limits on the vector-like top quark mass as a function of the branching ratios into $Wb$ and $Ht$.

Expected 95% CL lower limits on the vector-like bottom quark mass as a function of the branching ratios into $Hb$ and $Wt$.

Observed 95% CL lower limits on the vector-like top quark mass as a function of the branching ratios into $Hb$ and $Wt$.

Left panel.The vertical bars (boxes) represent the statistical (bin-to-bin systematic) uncertainties, while the horizontal bars give the bin widths. The global uncertainty (of 5.7%) is not graphically represented. The blue line represents the average of all the points. $ 12 < \mathrm{B} \, p_T < 70$ GeV and $ 0 < |y| < 2.4 $. Global uncertanties are not included in the table (5.7%)

Right panel.The vertical bars (boxes) represent the statistical (bin-to-bin systematic) uncertainties, while the horizontal bars give the bin widths. The global uncertainty (of 5.7%) is not graphically represented. The blue line represents the average of all the points. $ 12 < \mathrm{B} \, p_T < 70$ GeV and $ 0 < |y| < 2.4 $. Global uncertanties are not included in the table (5.7%)

Mass dependence of the mid-rapidity $v_1$ slope, $dv_1/dy$, for $\Lambda$, $^{3}_{\Lambda}$H and $^{4}_{\Lambda}$H from the $\sqrt{s_{NN}}$ = 3 GeV 5-40% mid-central Au+Au collisions. The statistical and systematic uncertainties are presented by vertical lines and square brackets, respectively. The slopes of $p$, $d$, $t$, $^3$He and $^4$He from the same collisions are shown as black circles. The blue and dashed green lines are the results of a linear fit to the measured light nuclei and hypernuclei $v_1$ slopes, respectively. For comparison, calculations of transport models plus coalescence afterburner are shown as gold and red bars from JAM model, and blue bars from UrQMD model.

Mass dependence of the mid-rapidity $v_1$ slope, $dv_1/dy$, for $\Lambda$, $^{3}_{\Lambda}$H and $^{4}_{\Lambda}$H from the $\sqrt{s_{NN}}$ = 3 GeV 5-40% mid-central Au+Au collisions. The statistical and systematic uncertainties are presented by vertical lines and square brackets, respectively. The slopes of $p$, $d$, $t$, $^3$He and $^4$He from the same collisions are shown as black circles. The blue and dashed green lines are the results of a linear fit to the measured light nuclei and hypernuclei $v_1$ slopes, respectively. For comparison, calculations of transport models plus coalescence afterburner are shown as gold and red bars from JAM model, and blue bars from UrQMD model.

Mass dependence of the mid-rapidity $v_1$ slope, $dv_1/dy$, for $\Lambda$, $^{3}_{\Lambda}$H and $^{4}_{\Lambda}$H from the $\sqrt{s_{NN}}$ = 3 GeV 5-40% mid-central Au+Au collisions. The statistical and systematic uncertainties are presented by vertical lines and square brackets, respectively. The slopes of $p$, $d$, $t$, $^3$He and $^4$He from the same collisions are shown as black circles. The blue and dashed green lines are the results of a linear fit to the measured light nuclei and hypernuclei $v_1$ slopes, respectively. For comparison, calculations of transport models plus coalescence afterburner are shown as gold and red bars from JAM model, and blue bars from UrQMD model.

Mass dependence of the mid-rapidity $v_1$ slope, $dv_1/dy$, for $\Lambda$, $^{3}_{\Lambda}$H and $^{4}_{\Lambda}$H from the $\sqrt{s_{NN}}$ = 3 GeV 5-40% mid-central Au+Au collisions. The statistical and systematic uncertainties are presented by vertical lines and square brackets, respectively. The slopes of $p$, $d$, $t$, $^3$He and $^4$He from the same collisions are shown as black circles. The blue and dashed green lines are the results of a linear fit to the measured light nuclei and hypernuclei $v_1$ slopes, respectively. For comparison, calculations of transport models plus coalescence afterburner are shown as gold and red bars from JAM model, and blue bars from UrQMD model.

Mass dependence of the mid-rapidity $v_1$ slope, $dv_1/dy$, for $\Lambda$, $^{3}_{\Lambda}$H and $^{4}_{\Lambda}$H from the $\sqrt{s_{NN}}$ = 3 GeV 5-40% mid-central Au+Au collisions. The statistical and systematic uncertainties are presented by vertical lines and square brackets, respectively. The slopes of $p$, $d$, $t$, $^3$He and $^4$He from the same collisions are shown as black circles. The blue and dashed green lines are the results of a linear fit to the measured light nuclei and hypernuclei $v_1$ slopes, respectively. For comparison, calculations of transport models plus coalescence afterburner are shown as gold and red bars from JAM model, and blue bars from UrQMD model.

Charged-hadron spectrum in the centrality interval 5-10% for p+Pb, divided by &#9001;TPPB&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 10-20% for p+Pb, divided by &#9001;TPPB&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 20-30% for p+Pb, divided by &#9001;TPPB&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 30-40% for p+Pb, divided by &#9001;TPPB&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 40-60% for p+Pb, divided by &#9001;TPPB&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 60-90% for p+Pb, divided by &#9001;TPPB&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 0-90% for p+Pb, divided by &#9001;TPPB&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron cross-section in pp collisions. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 0-5% for Pb+Pb, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Charged-hadron spectrum in the centrality interval 5-10% for Pb+Pb, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Charged-hadron spectrum in the centrality interval 10-20% for Pb+Pb, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Charged-hadron spectrum in the centrality interval 20-30% for Pb+Pb, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Charged-hadron spectrum in the centrality interval 30-40% for Pb+Pb, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Charged-hadron spectrum in the centrality interval 40-50% for Pb+Pb, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Charged-hadron spectrum in the centrality interval 50-60% for Pb+Pb, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Charged-hadron spectrum in the centrality interval 60-80% for Pb+Pb, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Charged-hadron cross-section in pp collisions. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 0-5% for Xe+Xe, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 5-10% for Xe+Xe, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 10-20% for Xe+Xe, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 20-30% for Xe+Xe, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 30-40% for Xe+Xe, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 40-50% for Xe+Xe, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 50-60% for Xe+Xe, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Charged-hadron spectrum in the centrality interval 60-80% for Xe+Xe, divided by &#9001;TAA&#9002;. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-60% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Nuclear modification factor in centrality interval 5-10% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Nuclear modification factor in centrality interval 10-20% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Nuclear modification factor in centrality interval 20-30% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Nuclear modification factor in centrality interval 30-40% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Nuclear modification factor in centrality interval 40-50% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Nuclear modification factor in centrality interval 50-60% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Nuclear modification factor in centrality interval 60-80% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature. The systematic uncertainty on momentum bias is negligible at low pT; in such cases, it is omitted in the table below.

Nuclear modification factor in centrality interval 0-5% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-50% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 50-60% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-80% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-60% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-60% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-60% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-60% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-90% for p+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-50% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-50% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-50% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-50% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 50-60% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 50-60% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 50-60% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 50-60% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-80% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-80% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-80% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-80% for Pb+Pb. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 0-5% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 5-10% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 10-20% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 20-30% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 30-40% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-50% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-50% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 40-50% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 50-60% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 50-60% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 50-60% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-80% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-80% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.

Nuclear modification factor in centrality interval 60-80% for Xe+Xe. The systematic uncertainties are described in the section 7 of the paper. The total systematic uncertainties are determined by adding the contributions from all relevant sources in quadrature.