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| CMS-PAS-TOP-21-008 | ||
| Measurement of the top quark pole mass using $\text{t}\overline{\text{t}}$+jet events in the dilepton final state at $\sqrt{s}=$ 13 TeV | ||
| CMS Collaboration | ||
| March 2022 | ||
| Abstract: A measurement of the top quark pole mass $m_{\text{t}}^{\text{pole}}$ in events where a top quark-antiquark pair ($\text{t}\overline{\text{t}}$) is produced in association with one additional jet ($\text{t}\overline{\text{t}}$+jet) is presented. This analysis is performed using proton-proton collision data at $\sqrt{s}=$ 13 TeV collected by the CMS experiment at the CERN LHC, corresponding to a total integrated luminosity of 36.3fb$^{-1}$. Events with two opposite charge leptons in the final state ($\text{e}^{+}\text{e}^{-}$, $\mu^{+}\mu^{-}$, $\text{e}^{\pm}\mu^{\mp}$) are analyzed. Using multivariate analysis techniques based on machine learning, the reconstruction of the main observable and the event selection are optimized. The production cross section is measured as a function of the inverse invariant mass of the $\text{t}\overline{\text{t}}$+jet system at the parton level, using a maximum likelihood unfolding. The top quark pole mass is extracted using the theory predictions at next-to-leading order, resulting in $m_{\text{t}}^{\text{pole}}=$ 172.94 $\pm$ 1.37 GeV when using the ABMP16NLO parton distribution functions. | ||
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Links:
CDS record (PDF) ;
CADI line (restricted) ;
These preliminary results are superseded in this paper, Submitted to JHEP. The superseded preliminary plots can be found here. |
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| Figures | |
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Figure 1:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the number of leading (upper left) and trailing (upper middle) lepton ${p_{\mathrm {T}}}$, leading (upper right) and third hardest (lower left) jet ${p_{\mathrm {T}}}$, the jet multiplicity (lower middle), and b tagged jet multiplicity (lower right), after applying the signal selection. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 1-a:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the number of leading (upper left) and trailing (upper middle) lepton ${p_{\mathrm {T}}}$, leading (upper right) and third hardest (lower left) jet ${p_{\mathrm {T}}}$, the jet multiplicity (lower middle), and b tagged jet multiplicity (lower right), after applying the signal selection. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 1-b:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the number of leading (upper left) and trailing (upper middle) lepton ${p_{\mathrm {T}}}$, leading (upper right) and third hardest (lower left) jet ${p_{\mathrm {T}}}$, the jet multiplicity (lower middle), and b tagged jet multiplicity (lower right), after applying the signal selection. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 1-c:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the number of leading (upper left) and trailing (upper middle) lepton ${p_{\mathrm {T}}}$, leading (upper right) and third hardest (lower left) jet ${p_{\mathrm {T}}}$, the jet multiplicity (lower middle), and b tagged jet multiplicity (lower right), after applying the signal selection. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 1-d:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the number of leading (upper left) and trailing (upper middle) lepton ${p_{\mathrm {T}}}$, leading (upper right) and third hardest (lower left) jet ${p_{\mathrm {T}}}$, the jet multiplicity (lower middle), and b tagged jet multiplicity (lower right), after applying the signal selection. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 1-e:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the number of leading (upper left) and trailing (upper middle) lepton ${p_{\mathrm {T}}}$, leading (upper right) and third hardest (lower left) jet ${p_{\mathrm {T}}}$, the jet multiplicity (lower middle), and b tagged jet multiplicity (lower right), after applying the signal selection. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 1-f:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the number of leading (upper left) and trailing (upper middle) lepton ${p_{\mathrm {T}}}$, leading (upper right) and third hardest (lower left) jet ${p_{\mathrm {T}}}$, the jet multiplicity (lower middle), and b tagged jet multiplicity (lower right), after applying the signal selection. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 2:
The correlation between $\rho _{\text {true}}$ and $\rho _{\text {reco}}$ is shown for the regression NN reconstruction method (left). The distribution of the response corrected RMS (right) of $\rho _{\text {true}}-\rho _{\text {reco}}$ versus $\rho _{\text {true}}$ is shown, compared for the kinematic reconstruction (blue), the loose kinematic reconstruction (orange), and the regression NN (red). |
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Figure 2-a:
The correlation between $\rho _{\text {true}}$ and $\rho _{\text {reco}}$ is shown for the regression NN reconstruction method (left). The distribution of the response corrected RMS (right) of $\rho _{\text {true}}-\rho _{\text {reco}}$ versus $\rho _{\text {true}}$ is shown, compared for the kinematic reconstruction (blue), the loose kinematic reconstruction (orange), and the regression NN (red). |
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Figure 2-b:
The correlation between $\rho _{\text {true}}$ and $\rho _{\text {reco}}$ is shown for the regression NN reconstruction method (left). The distribution of the response corrected RMS (right) of $\rho _{\text {true}}-\rho _{\text {reco}}$ versus $\rho _{\text {true}}$ is shown, compared for the kinematic reconstruction (blue), the loose kinematic reconstruction (orange), and the regression NN (red). |
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Figure 3:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function $\rho $ as reconstructed by the NN reconstruction method for the $\mathrm{e^{\pm}} {\mu ^\mp} $ (left) and same flavor channels (right). The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 3-a:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function $\rho $ as reconstructed by the NN reconstruction method for the $\mathrm{e^{\pm}} {\mu ^\mp} $ (left) and same flavor channels (right). The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 3-b:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function $\rho $ as reconstructed by the NN reconstruction method for the $\mathrm{e^{\pm}} {\mu ^\mp} $ (left) and same flavor channels (right). The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 4:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the signal (left) and $\mathrm{t\bar{t}}$+jet background (right) output node probability of the classifier NN. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 4-a:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the signal (left) and $\mathrm{t\bar{t}}$+jet background (right) output node probability of the classifier NN. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 4-b:
The observed (points) and the predicted (stacked histograms) signal and background yields as a function of the signal (left) and $\mathrm{t\bar{t}}$+jet background (right) output node probability of the classifier NN. The shaded area indicates the total uncertainties on the signal and total background, while the dark gray area represents the statistical uncertainty from the limited amount of events simulated. |
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Figure 5:
Comparison between data (points) and pre-fit distributions of the expected signal and backgrounds from simulation (histograms) used in the simultaneous fit. The hatched bands corresponds to the total uncertainty. |
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Figure 6:
Comparison between data (points) and post-fit distributions of the expected signal and backgrounds from simulation (histograms) used in the simultaneous fit. The hatched bands corresponds to the total uncertainty. |
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Figure 7:
Post-fit pull of the nuisance parameters as well as their impacts on the signal strengths $r_k$, ordered by their relative summed impact. Only the 30 highest ranked parameters are shown. The pulls of the nuisance parameters (black markers) are computed relative to their pre-fit values $\theta _0$ and uncertainties $\Delta \theta $. The impact $\Delta r_{k}$ are computed as the difference of the nominal best fit value of $r_k$ and the best fit value obtained when fixing the nuisance parameter under scrutiny to its bestfit value $\hat{\theta}$ plus/minus its post-fit uncertainty (colored areas). The shaded areas correspond to the expected pulls, constraints, and impacts. |
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Figure 8:
The absolute (left) and normalized (right) differential cross section as a function of $\rho $. The gray bands correspond to the total uncertainty, while the errors of the data points correspond to the statistical only uncertainty. The blue line shows the expected result using the nominal POWHEG+PYTHIA-8 simulation, while the red, orange, and green lines correspond to the expectation from NLO fixed order theory prediction for top mass assumptions of 169.5, 172.5, and 175.5 GeV, respectively. |
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Figure 8-a:
The absolute (left) and normalized (right) differential cross section as a function of $\rho $. The gray bands correspond to the total uncertainty, while the errors of the data points correspond to the statistical only uncertainty. The blue line shows the expected result using the nominal POWHEG+PYTHIA-8 simulation, while the red, orange, and green lines correspond to the expectation from NLO fixed order theory prediction for top mass assumptions of 169.5, 172.5, and 175.5 GeV, respectively. |
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Figure 8-b:
The absolute (left) and normalized (right) differential cross section as a function of $\rho $. The gray bands correspond to the total uncertainty, while the errors of the data points correspond to the statistical only uncertainty. The blue line shows the expected result using the nominal POWHEG+PYTHIA-8 simulation, while the red, orange, and green lines correspond to the expectation from NLO fixed order theory prediction for top mass assumptions of 169.5, 172.5, and 175.5 GeV, respectively. |
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Figure 9:
Left: ${\chi ^2}$ fit of the top quark pole mass using the measured normalized differential cross section and NLO fixed order theory prediction using the ABMP16NLO (blue) and the CT18NLO PDF set (red) The ${\chi ^2}$ values at the minimum are given divided by the number of degrees of freedom (NDF). Right: The normalized differential cross section as a function of $\rho $, same as in Fig. 8, compared to predictions for top quark pole mass values as determined in the ${\chi ^2}$ fit including scale and PDF uncertainties (hatched bands). The gray bands correspond to the total fit uncertainty. |
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Figure 9-a:
Left: ${\chi ^2}$ fit of the top quark pole mass using the measured normalized differential cross section and NLO fixed order theory prediction using the ABMP16NLO (blue) and the CT18NLO PDF set (red) The ${\chi ^2}$ values at the minimum are given divided by the number of degrees of freedom (NDF). Right: The normalized differential cross section as a function of $\rho $, same as in Fig. 8, compared to predictions for top quark pole mass values as determined in the ${\chi ^2}$ fit including scale and PDF uncertainties (hatched bands). The gray bands correspond to the total fit uncertainty. |
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Figure 9-b:
Left: ${\chi ^2}$ fit of the top quark pole mass using the measured normalized differential cross section and NLO fixed order theory prediction using the ABMP16NLO (blue) and the CT18NLO PDF set (red) The ${\chi ^2}$ values at the minimum are given divided by the number of degrees of freedom (NDF). Right: The normalized differential cross section as a function of $\rho $, same as in Fig. 8, compared to predictions for top quark pole mass values as determined in the ${\chi ^2}$ fit including scale and PDF uncertainties (hatched bands). The gray bands correspond to the total fit uncertainty. |
| Tables | |
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Table 1:
Overview about categories and distributions implemented in the maximum likelihood fit. The symbol ${R_{\text {NN}}}$ is defined as $ R_{\text {NN}} = {p({\mathrm{t\bar{t}}\text{+jet})}}/{[p({\mathrm{t\bar{t}}\text{+jet})+p({\mathrm{t\bar{t}}+jet)}}]} $. |
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Table 2:
The relative uncertainties on the parton-level cross section values $\sigma _{\mathrm{t\bar{t}}\text{+jet}}^{k}$ and their sources. The statistical uncertainty is evaluated by keeping all nuisance parameters fixed to their post-fit values. All other quoted uncertainties are obtained by fixing all but the listed sources of uncertainties to their post-fit values and subtracting the result quadratically from a fit with all nuisance parameters fixed to their post-fit values. The quadratic sum of the contributions varies from the total uncertainty because of correlations between the nuisance parameters. |
| Summary |
| In this note, a measurement of the normalized differential cross section of top quark-antiquark pair ($\mathrm{t\bar{t}}$) production in association with at least one additional jet as a function of the inverse invariant mass of the $\mathrm{t\bar{t}}$+jet system $\rho=m_0/m_{\mathrm{t\bar{t}}\text{+jet}}$, with $m_0 = $ 170 GeV, is presented. Proton-proton collision data collected by the CMS experiment at the CERN LHC at a center-of-mass energy of 13 TeV are used, corresponding to an integrated luminosity of 36.3 fb$^{-1}$. Events in the dileptonic decay channel are considered, and a novel multivariate analysis technique is applied to maximize the sensitivity to the signal process. The measured differential cross section is unfolded to the parton level by using a maximum likelihood fit to final-state observables, where all systematic uncertainties are profiled. From a comparison to next-to-leading order prediction in quantum chromodynamics in the pole mass renormalization scheme, the top quark pole mass is extracted using two sets of parton distribution functions. Dynamical scales are used here for the first time for the $\mathrm{t\bar{t}}$+jet process, and the top quark pole mass is determined to be ${m_{\mathrm{t}}^{\text{pole}}} = $ 172.94$^{+1.37}_{-1.34}$ GeV using the ABMP16NLO PDF set, and ${m_{\mathrm{t}}^{\text{pole}}} = $ 172.16$^{+1.44}_{-1.41}$ GeV using the CT18NLO PDF set. |
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