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| CMS-PAS-HIN-24-021 | ||
| Measurement of the top quark pair production cross section in PbPb collisions at $ \sqrt{\smash[b]{s_{_{\mathrm{NN}}}}} = $ 5.36 TeV | ||
| CMS Collaboration | ||
| 2025-09-09 | ||
| Abstract: The first measurement of the production cross section of top quark pairs ($ {\rm t\bar{t}} $) in lead-lead collisions at $ \sqrt{s_{\rm NN}}= $ 5.36 TeV is presented. The data were collected by the CMS experiment in 2023, corresponding to an integrated luminosity of 1.58 nb$ ^{-1} $. A cross section of $ \sigma_{\rm t\bar{t}} = 3.42 ^{+0.54}_{-0.50} {\rm (stat)} ^{+0.50}_{-0.43} {\rm (syst)} \mu{\rm b} $ is measured from a fit to a multivariate discriminator based on the lepton kinematic distributions and the number of jets associated with bottom quarks, and found to be in agreement with predictions using state-of-the-art nuclear parton distribution functions. In addition, the ratio of the $ {\rm t\bar{t}} $ to Drell-Yan (DY) cross sections is determined to be $ R_{\rm t\bar{t}/Z} = 0.0086 ^{+0.0014}_{-0.0013} {\rm (stat)} ^{+0.0011}_{-0.0010} {\rm (syst)} $, with improved precision relative to the $ \sigma_{\rm t\bar{t}} $ extraction and in agreement with predictions. Both $ \sigma_{\rm t\bar{t}} $ and $ R_{\rm t\bar{t}/Z} $ are measured as function of the centrality, a quantity which quantify the degree of overlap between the colliding nuclei. | ||
| Links: CDS record (PDF) ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Distributions of events in the $ p_{\mathrm{T}}(\ell\ell) $ (left) and $ m(\ell\ell) $ (right) variables in the same flavor ($ \ell\ell $) channels. The $ m(\ell\ell) $ distribution is shown prior to the veto of the Z boson resonant region. The data (black marker) is overlaid on top of the stacked contribution of the expectations for the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes. Overflows are included in the last bin. |
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Figure 1-a:
Distributions of events in the $ p_{\mathrm{T}}(\ell\ell) $ (left) and $ m(\ell\ell) $ (right) variables in the same flavor ($ \ell\ell $) channels. The $ m(\ell\ell) $ distribution is shown prior to the veto of the Z boson resonant region. The data (black marker) is overlaid on top of the stacked contribution of the expectations for the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes. Overflows are included in the last bin. |
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Figure 1-b:
Distributions of events in the $ p_{\mathrm{T}}(\ell\ell) $ (left) and $ m(\ell\ell) $ (right) variables in the same flavor ($ \ell\ell $) channels. The $ m(\ell\ell) $ distribution is shown prior to the veto of the Z boson resonant region. The data (black marker) is overlaid on top of the stacked contribution of the expectations for the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes. Overflows are included in the last bin. |
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Figure 2:
Distributions of events in the $ p_{\mathrm{T}}(\ell_2) $ (upper-left), $ p_{\mathrm{T}}(\ell\ell) $ (upper-right) and $ \sum |\eta_\ell| $ (lower) variables in the opposite flavor ($ \mathrm{e}\mu $) channel. The data (black marker) is overlaid on top of the stacked contribution of the expectations for the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes. Overflows are included in the last bin. |
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Figure 2-a:
Distributions of events in the $ p_{\mathrm{T}}(\ell_2) $ (upper-left), $ p_{\mathrm{T}}(\ell\ell) $ (upper-right) and $ \sum |\eta_\ell| $ (lower) variables in the opposite flavor ($ \mathrm{e}\mu $) channel. The data (black marker) is overlaid on top of the stacked contribution of the expectations for the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes. Overflows are included in the last bin. |
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Figure 2-b:
Distributions of events in the $ p_{\mathrm{T}}(\ell_2) $ (upper-left), $ p_{\mathrm{T}}(\ell\ell) $ (upper-right) and $ \sum |\eta_\ell| $ (lower) variables in the opposite flavor ($ \mathrm{e}\mu $) channel. The data (black marker) is overlaid on top of the stacked contribution of the expectations for the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes. Overflows are included in the last bin. |
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Figure 2-c:
Distributions of events in the $ p_{\mathrm{T}}(\ell_2) $ (upper-left), $ p_{\mathrm{T}}(\ell\ell) $ (upper-right) and $ \sum |\eta_\ell| $ (lower) variables in the opposite flavor ($ \mathrm{e}\mu $) channel. The data (black marker) is overlaid on top of the stacked contribution of the expectations for the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes. Overflows are included in the last bin. |
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Figure 3:
Results of the fits to the $ R_{p_{\mathrm{T}}}=p_{\mathrm{T}}({\rm jet})/p_{\mathrm{T}}(\mathrm{Z}) $ in $ \text{Z+jet} $ events in centrality bins of 0 -10% (upper-left) and 10-90% (upper-right). The upper panels show the estimated contribution from the underlying event jets (gray histogram) and the signal (black dashed line) stacked. The lower panel displays the pull distribution. The lower panel shows the ratio of the average $ R_{p_{\mathrm{T}}} $ in data over the $ R_{p_{\mathrm{T}}} $ in simulation as a function of the collision centrality. A fit to an error function (blue dashed line) is superimposed. |
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Figure 3-a:
Results of the fits to the $ R_{p_{\mathrm{T}}}=p_{\mathrm{T}}({\rm jet})/p_{\mathrm{T}}(\mathrm{Z}) $ in $ \text{Z+jet} $ events in centrality bins of 0 -10% (upper-left) and 10-90% (upper-right). The upper panels show the estimated contribution from the underlying event jets (gray histogram) and the signal (black dashed line) stacked. The lower panel displays the pull distribution. The lower panel shows the ratio of the average $ R_{p_{\mathrm{T}}} $ in data over the $ R_{p_{\mathrm{T}}} $ in simulation as a function of the collision centrality. A fit to an error function (blue dashed line) is superimposed. |
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Figure 3-b:
Results of the fits to the $ R_{p_{\mathrm{T}}}=p_{\mathrm{T}}({\rm jet})/p_{\mathrm{T}}(\mathrm{Z}) $ in $ \text{Z+jet} $ events in centrality bins of 0 -10% (upper-left) and 10-90% (upper-right). The upper panels show the estimated contribution from the underlying event jets (gray histogram) and the signal (black dashed line) stacked. The lower panel displays the pull distribution. The lower panel shows the ratio of the average $ R_{p_{\mathrm{T}}} $ in data over the $ R_{p_{\mathrm{T}}} $ in simulation as a function of the collision centrality. A fit to an error function (blue dashed line) is superimposed. |
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Figure 3-c:
Results of the fits to the $ R_{p_{\mathrm{T}}}=p_{\mathrm{T}}({\rm jet})/p_{\mathrm{T}}(\mathrm{Z}) $ in $ \text{Z+jet} $ events in centrality bins of 0 -10% (upper-left) and 10-90% (upper-right). The upper panels show the estimated contribution from the underlying event jets (gray histogram) and the signal (black dashed line) stacked. The lower panel displays the pull distribution. The lower panel shows the ratio of the average $ R_{p_{\mathrm{T}}} $ in data over the $ R_{p_{\mathrm{T}}} $ in simulation as a function of the collision centrality. A fit to an error function (blue dashed line) is superimposed. |
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Figure 4:
Jet (upper row) and b jet (lower row) counting in Z boson candidate events. The left (right) plots correspond to events in the 0-10% (10-90%) centrality bin. Overflows are included in the last bin. The upper panels display the post-fit stacked model of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes, and the data (black marker). The bottom panels show the data-to-expectation ratios before (yellow line) and after (black marker) the fit. The dashed band represents the post-fit uncertainty of the model in each bin. |
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Figure 4-a:
Jet (upper row) and b jet (lower row) counting in Z boson candidate events. The left (right) plots correspond to events in the 0-10% (10-90%) centrality bin. Overflows are included in the last bin. The upper panels display the post-fit stacked model of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes, and the data (black marker). The bottom panels show the data-to-expectation ratios before (yellow line) and after (black marker) the fit. The dashed band represents the post-fit uncertainty of the model in each bin. |
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Figure 4-b:
Jet (upper row) and b jet (lower row) counting in Z boson candidate events. The left (right) plots correspond to events in the 0-10% (10-90%) centrality bin. Overflows are included in the last bin. The upper panels display the post-fit stacked model of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes, and the data (black marker). The bottom panels show the data-to-expectation ratios before (yellow line) and after (black marker) the fit. The dashed band represents the post-fit uncertainty of the model in each bin. |
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Figure 4-c:
Jet (upper row) and b jet (lower row) counting in Z boson candidate events. The left (right) plots correspond to events in the 0-10% (10-90%) centrality bin. Overflows are included in the last bin. The upper panels display the post-fit stacked model of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes, and the data (black marker). The bottom panels show the data-to-expectation ratios before (yellow line) and after (black marker) the fit. The dashed band represents the post-fit uncertainty of the model in each bin. |
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Figure 4-d:
Jet (upper row) and b jet (lower row) counting in Z boson candidate events. The left (right) plots correspond to events in the 0-10% (10-90%) centrality bin. Overflows are included in the last bin. The upper panels display the post-fit stacked model of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes, and the data (black marker). The bottom panels show the data-to-expectation ratios before (yellow line) and after (black marker) the fit. The dashed band represents the post-fit uncertainty of the model in each bin. |
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Figure 5:
Distributions of the final discriminator after the fit for the same flavor $ \ell\ell $ (upper row) and opposite flavor $ \mathrm{e}\mu $ (lower row) channels. The discriminator output is shown for 0, 1 and $ \geq $ 2 b-tag jet events. The left (right) figure corresponds to events reconstructed in the 0-10% (10-90%) centrality bins. The panels display the stacked contributions of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes normalized to the post-fit expectations and the data (black marker). The gray band shows the total uncertainty in the predicted yields after the fit is performed. |
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Figure 5-a:
Distributions of the final discriminator after the fit for the same flavor $ \ell\ell $ (upper row) and opposite flavor $ \mathrm{e}\mu $ (lower row) channels. The discriminator output is shown for 0, 1 and $ \geq $ 2 b-tag jet events. The left (right) figure corresponds to events reconstructed in the 0-10% (10-90%) centrality bins. The panels display the stacked contributions of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes normalized to the post-fit expectations and the data (black marker). The gray band shows the total uncertainty in the predicted yields after the fit is performed. |
png pdf |
Figure 5-b:
Distributions of the final discriminator after the fit for the same flavor $ \ell\ell $ (upper row) and opposite flavor $ \mathrm{e}\mu $ (lower row) channels. The discriminator output is shown for 0, 1 and $ \geq $ 2 b-tag jet events. The left (right) figure corresponds to events reconstructed in the 0-10% (10-90%) centrality bins. The panels display the stacked contributions of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes normalized to the post-fit expectations and the data (black marker). The gray band shows the total uncertainty in the predicted yields after the fit is performed. |
png pdf |
Figure 5-c:
Distributions of the final discriminator after the fit for the same flavor $ \ell\ell $ (upper row) and opposite flavor $ \mathrm{e}\mu $ (lower row) channels. The discriminator output is shown for 0, 1 and $ \geq $ 2 b-tag jet events. The left (right) figure corresponds to events reconstructed in the 0-10% (10-90%) centrality bins. The panels display the stacked contributions of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes normalized to the post-fit expectations and the data (black marker). The gray band shows the total uncertainty in the predicted yields after the fit is performed. |
png pdf |
Figure 5-d:
Distributions of the final discriminator after the fit for the same flavor $ \ell\ell $ (upper row) and opposite flavor $ \mathrm{e}\mu $ (lower row) channels. The discriminator output is shown for 0, 1 and $ \geq $ 2 b-tag jet events. The left (right) figure corresponds to events reconstructed in the 0-10% (10-90%) centrality bins. The panels display the stacked contributions of the $ \mathrm{t} \overline{\mathrm{t}} $ (blue), nonprompt (yellow), WW (red), single top (gray) and DY (violet) processes normalized to the post-fit expectations and the data (black marker). The gray band shows the total uncertainty in the predicted yields after the fit is performed. |
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Figure 6:
Scan of the profile likelihood as a function of the $ \mathrm{t} \overline{\mathrm{t}} $ signal strength. The expected and observed scans are overlaid. In each case, two additional curves corresponding to the scan resulting from freezing the systematic uncertainties are overlaid. The horizontal dashed lines represent the likelihood values (confidence level - CL) used to extract the 68% and 95% confidence intervals on the parameter of interest. |
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Figure 7:
Impact of systematic uncertainties on the fitted $ \mathrm{t} \overline{\mathrm{t}} $ strength parameter. The systematic uncertainties are listed in decreasing order of their impact on the strength parameter in the vertical axis. |
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Figure 8:
Comparison between the measured $ \mathrm{t} \overline{\mathrm{t}} $ cross section (shaded area) and the SM theory predictions (black markers) employing the TUJU21 [53], nNNPDF30 [54], nNNPDF10 [55] and EPPS21 [21] nuclear PDF sets. The error bars represent the uncertainties of the theory calculations while the blue (orange) area shows the total (statistical) uncertainties of the measured $ \mathrm{t} \overline{\mathrm{t}} $ cross section. |
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Figure 9:
Comparison between the measured ratio of $ \mathrm{t} \overline{\mathrm{t}} $ over DY cross sections (shaded area) and the Standard Model theory predictions (black markers) employing the TUJU21 [53] and EPPS21 [21] nuclear PDF sets. The error bars represent the uncertainties of the theory calculations while the blue (orange) area shows the total (statistical) uncertainties of the measured $ \mathrm{t} \overline{\mathrm{t}} $ over DY ratio. |
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Figure 10:
Comparison between the measured $ \mathrm{t} \overline{\mathrm{t}} $ cross section in PbPb at 5.36 TeV and the past measurements performed by the ATLAS and CMS collaborations in PbPb at 5.02 TeV and pp at 5.02 TeV scaled by the Pb mass number (A) squared. The error bars on each marker represent the statistical and total uncertainties. |
| Summary |
| We have presented the first measurement of the production cross section of top quark pairs in lead-lead collisions, $ \sigma(\mathrm{Pb}\mathrm{Pb}\rightarrow{\mathrm{t}\overline{\mathrm{t}}} ) $, at $ \sqrt{\smash[b]{s_{_{\mathrm{NN}}}}}= $ 5.36 TeV. Using dilepton events, selected in the 2023 data set recorded by the CMS experiment, the $ \sigma(\mathrm{Pb}\mathrm{Pb}\rightarrow{\mathrm{t}\overline{\mathrm{t}}} ) $, along with its ratio to the Drell-Yan cross section, are measured with total uncertainties of approximately 21%, and are both found to be in agreement with the standard model. New techniques used in this analysis, such as the electron identification criteria, the lepton isolation criteria, the calibration of the jet energy scale, and the identification of heavy-flavor jets, lead to a significant improvement in precision compared to previous $ \sigma(\mathrm{Pb}\mathrm{Pb}\rightarrow{\mathrm{t}\overline{\mathrm{t}}} ) $ measurements at the LHC. Combined with the increased center-of-mass energy and integrated luminosity expected for the remainder of Run 3, these advances establish top quark physics as a precision tool for studying the formation and properties of the quark-gluon plasma in heavy ion collisions. |
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