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| CMS-PAS-TOP-17-015 | ||
| Study of the underlying event in top quark pair production at 13 TeV | ||
| CMS Collaboration | ||
| March 2018 | ||
| Abstract: Normalized differential cross sections as functions of the multiplicity and kinematic variables of charged-particle tracks from the underlying event are measured in top quark and antiquark pair events produced in proton-proton collisions at a center-of-mass energy of 13 TeV. The analysis is based on data collected by the CMS experiment at the LHC in 2016 and corresponding to an integrated luminosity of 35.9 fb$^{-1}$. The selected events contain one electron, one muon, and two jets from the hadronization and fragmentation of b quarks. These measurements characterize, for the first time, the properties of the underlying event at a factorization scale which is typically above twice the top quark mass. The sensitivity of the measured cross sections to different parameters employed in state-of-the-art Monte Carlo simulation programs is demonstrated by comparing the results with different simulations. | ||
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Links:
CDS record (PDF) ;
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These preliminary results are superseded in this paper, EPJC 79 (2019) 123. |
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| Figures & Tables | Summary | Additional Figures & Tables | References | CMS Publications |
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| Figures | |
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Figure 1:
Distribution of all PF candidates reconstructed in a simulated $ {{\mathrm {t}\overline {\mathrm {t}}}} $ event on the $\eta -\phi $ plane. Only particles with $ {p_{\mathrm {T}}} > $ 900 MeV are shown and the $ {p_{\mathrm {T}}} $ of the particles is proportional to the area of the markers. The fiducial region in $\eta $ is represented by dashed black lines. |
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Figure 2:
Display of the momentum of the selected charged particles, the two leptons, and the dilepton pair in the transverse plane corresponding to the same simulated event represented in Fig. 1. The $ {p_{\mathrm {T}}} $ of the particles is proportional to the length of the arrows and the dashed lines represent the regions that are defined relatively to the transverse momentum of the dilepton pair. For clarity, the $ {p_{\mathrm {T}}} $ of the leptons has been rescaled by 0.5. |
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Figure 3:
Distributions of the variables used to categorize the study of the UE. Left: multiplicity of additional jets ($ {p_{\mathrm {T}}} > $ 30 GeV). Center: transverse momentum of the dilepton pair. Right: invariant mass of the dilepton pair. The distributions from the data are compared to the sum of the expectations for the signal and backgrounds. |
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Figure 3-a:
Distribution of one of the variables used to categorize the study of the UE: multiplicity of additional jets ($ {p_{\mathrm {T}}} > $ 30 GeV). The distribution from the data is compared to the sum of the expectations for the signal and backgrounds. |
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Figure 3-b:
Distribution of one of the variables used to categorize the study of the UE: transverse momentum of the dilepton pair. The distribution from the data is compared to the sum of the expectations for the signal and backgrounds. |
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Figure 3-c:
Distribution of one of the variables used to categorize the study of the UE: invariant mass of the dilepton pair. The distribution from the data is compared to the sum of the expectations for the signal and backgrounds. |
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Figure 4:
The normalized differential cross section as function of $N_{\rm ch}$ is shown on the left. The data (colored boxes) are compared to the nominal Pw+Py8 expectations and to the expectations obtained from varied $ {\alpha _S} ^{\rm ISR}$ or $ {\alpha _S} ^{\rm FSR}$ Pw+Py8 setups (markers). Different panels shown on the right display the ratio between each model tested (see text) and the data. In both cases the colored (shaded) band represents the total (statistical) uncertainty of the data, while the error bars represent either the total uncertainty of the Pw+Py8 setup, computed as described in the text, or the statistical uncertainty of the other MC setups. |
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Figure 4-a:
The normalized differential cross section as function of $N_{\rm ch}$. The data (colored boxes) are compared to the nominal Pw+Py8 expectations and to the expectations obtained from varied $ {\alpha _S} ^{\rm ISR}$ or $ {\alpha _S} ^{\rm FSR}$ Pw+Py8 setups (markers). |
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Figure 4-b:
The normalized differential cross section as function of $N_{\rm ch}$. Different panels display the ratio between each model tested (see text) and the data. In both cases the colored (shaded) band represents the total (statistical) uncertainty of the data, while the error bars represent either the total uncertainty of the Pw+Py8 setup, computed as described in the text, or the statistical uncertainty of the other MC setups. |
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Figure 5:
Normalized differential cross section as function of $ \Sigma p_{\rm T}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 5-a:
Normalized differential cross section as function of $ \Sigma p_{\rm T}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 5-b:
Normalized differential cross section as function of $ \Sigma p_{\rm T}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 6:
Normalized differential cross section as function of $\bar{p}_{\rm T}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 6-a:
Normalized differential cross section as function of $\bar{p}_{\rm T}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 6-b:
Normalized differential cross section as function of $\bar{p}_{\rm T}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 7:
Normalized differential cross section as function of $|\vec{p}_{\rm T}|$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 7-a:
Normalized differential cross section as function of $|\vec{p}_{\rm T}|$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 7-b:
Normalized differential cross section as function of $|\vec{p}_{\rm T}|$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 8:
Normalized differential cross section as function of $\Sigma p_{z}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 8-a:
Normalized differential cross section as function of $\Sigma p_{z}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 8-b:
Normalized differential cross section as function of $\Sigma p_{z}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 9:
Normalized differential cross section as function of $\bar{p}_{z}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 9-a:
Normalized differential cross section as function of $\bar{p}_{z}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 9-b:
Normalized differential cross section as function of $\bar{p}_{z}$, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 10:
Normalized differential cross section as function of the sphericity variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 10-a:
Normalized differential cross section as function of the sphericity variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 10-b:
Normalized differential cross section as function of the sphericity variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 11:
Normalized differential cross section as function of the aplanarity variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 11-a:
Normalized differential cross section as function of the aplanarity variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 11-b:
Normalized differential cross section as function of the aplanarity variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 12:
Normalized differential cross section as function of the $C$ variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 12-a:
Normalized differential cross section as function of the $C$ variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 12-b:
Normalized differential cross section as function of the $C$ variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 13:
Normalized differential cross section as function of the $D$ variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 13-a:
Normalized differential cross section as function of the $D$ variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 13-b:
Normalized differential cross section as function of the $D$ variable, compared to the predictions of different models. The conventions of Fig. 4 are used. |
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Figure 14:
Average $N_{\rm ch}$ in different event categories. The mean observed in data (boxes) is compared to the predictions from different models (markers), which are superimposed in the left figure. The total (statistical) uncertainty of the data is represented by a filled (dashed) area and the statistical uncertainty of the models is represented with error bars. In the specific case of the Pw+Py8 model the error bars represent the total uncertainty (see text). The right figure displays the pull between the different models and the data, with the different panels corresponding to different sets of models. |
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Figure 14-a:
Average $N_{\rm ch}$ in different event categories. The mean observed in data (boxes) is compared to the predictions from different models (markers), which are superimposed in the figure. The total (statistical) uncertainty of the data is represented by a filled (dashed) area and the statistical uncertainty of the models is represented with error bars. In the specific case of the Pw+Py8 model the error bars represent the total uncertainty (see text). |
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Figure 14-b:
Average $N_{\rm ch}$ in different event categories. The figure displays the pull between the different models and the data, with the different panels corresponding to different sets of models. |
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Figure 15:
Average $ \Sigma p_{\rm T}$ in different event categories. The conventions of Fig. 14 are used. |
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Figure 15-a:
Average $ \Sigma p_{\rm T}$ in different event categories. The conventions of Fig. 14 are used. |
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Figure 15-b:
Average $ \Sigma p_{\rm T}$ in different event categories. The conventions of Fig. 14 are used. |
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Figure 16:
Average $\Sigma p_{z}$ in different categories. The conventions of Fig. 14 are used. |
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Figure 16-a:
Average $\Sigma p_{z}$ in different categories. The conventions of Fig. 14 are used. |
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Figure 16-b:
Average $\Sigma p_{z}$ in different categories. The conventions of Fig. 14 are used. |
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Figure 17:
Average $\bar{p}_{\rm T}$ in different categories. The conventions of Fig. 14 are used. |
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Figure 17-a:
Average $\bar{p}_{\rm T}$ in different categories. The conventions of Fig. 14 are used. |
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Figure 17-b:
Average $\bar{p}_{\rm T}$ in different categories. The conventions of Fig. 14 are used. |
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Figure 18:
Average $\bar{p}_{z}$ in different categories. The conventions of Fig. 14 are used. |
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Figure 18-a:
Average $\bar{p}_{z}$ in different categories. The conventions of Fig. 14 are used. |
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Figure 18-b:
Average $\bar{p}_{z}$ in different categories. The conventions of Fig. 14 are used. |
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Figure 19:
Average $|\vec{p}_{\rm T}|$ in different categories. The conventions of Fig. 14 are used. |
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Figure 19-a:
Average $|\vec{p}_{\rm T}|$ in different categories. The conventions of Fig. 14 are used. |
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Figure 19-b:
Average $|\vec{p}_{\rm T}|$ in different categories. The conventions of Fig. 14 are used. |
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Figure 20:
Average sphericity in different categories. The conventions of Fig. 14 are used. |
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Figure 20-a:
Average sphericity in different categories. The conventions of Fig. 14 are used. |
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Figure 20-b:
Average sphericity in different categories. The conventions of Fig. 14 are used. |
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Figure 21:
Average aplanarity in different categories. The conventions of Fig. 14 are used. |
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Figure 21-a:
Average aplanarity in different categories. The conventions of Fig. 14 are used. |
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Figure 21-b:
Average aplanarity in different categories. The conventions of Fig. 14 are used. |
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Figure 22:
Average C in different categories. The conventions of Fig. 14 are used. |
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Figure 22-a:
Average C in different categories. The conventions of Fig. 14 are used. |
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Figure 22-b:
Average C in different categories. The conventions of Fig. 14 are used. |
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Figure 23:
Average D in different categories. The conventions of Fig. 14 are used. |
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Figure 23-a:
Average D in different categories. The conventions of Fig. 14 are used. |
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Figure 23-b:
Average D in different categories. The conventions of Fig. 14 are used. |
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Figure 24:
Average $\bar{p}_{\rm T}$ in different $|\vec{p}_{\rm T}(\ell \ell)|$ categories. The conventions of Fig. 14 are used. |
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Figure 24-a:
Average $\bar{p}_{\rm T}$ in different $|\vec{p}_{\rm T}(\ell \ell)|$ categories. The conventions of Fig. 14 are used. |
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Figure 24-b:
Average $\bar{p}_{\rm T}$ in different $|\vec{p}_{\rm T}(\ell \ell)|$ categories. The conventions of Fig. 14 are used. |
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Figure 25:
Average $\bar{p}_{\rm T}$ in different $|\vec{p}_{\rm T}(\ell \ell)|$ and jet multiplicity categories. The conventions of Fig. 14 are used. |
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Figure 25-a:
Average $\bar{p}_{\rm T}$ in different $|\vec{p}_{\rm T}(\ell \ell)|$ and jet multiplicity categories. The conventions of Fig. 14 are used. |
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Figure 25-b:
Average $\bar{p}_{\rm T}$ in different $|\vec{p}_{\rm T}(\ell \ell)|$ and jet multiplicity categories. The conventions of Fig. 14 are used. |
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Figure 26:
Scan of the $\chi ^2$ as function of the value of $ {\alpha _S} ^{\rm FSR}$ employed in the Pw+Py8 simulation, when the inclusive $\bar{p}_{\rm T}$ or the $\bar{p}_{\rm T}$ distribution measured in different regions are used. The curves result from a fourth order polynomial interpolation between the simulated $ {\alpha _S} ^{\rm FSR}$ points. For the curve corresponding to the inclusive $\bar{p}_{\rm T}$ distribution, the points mark the simulated $ {\alpha _S} ^{\rm FSR}$ values. |
| Tables | |
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Table 1:
Monte Carlo setups used for the comparisons with the differential cross section measurements of the UE. The table lists the main characteristics and values used for the most relevant parameters of the generators. The row labeled as "Setup designation'' is used to define the abbreviation to be used throughout this paper. |
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Table 2:
Uncertainties affecting the measurement of the average of the UE observables. The values are expressed in % and the last row reports the quadratic sum of the individual contributions. |
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Table 3:
Comparison between the measured distributions at particle level and the predictions of different generator setups. We list the results of the $\chi ^2$ tests together with dof for different MC setups. For the comparison no uncertainties in the predictions are taken into account, except for the Pw+Py8 setup for which the comparison including the theory uncertainties is quoted separately in parenthesis. |
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Table 4:
The best fit values for $ {\alpha _S} ^{\rm FSR}$ for the Pw+Py8 setup, obtained from the inclusive distribution of different observables. The 68% and 95.45% confidence intervals are quoted in the last rows. |
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Table 5:
Variations of the Pw+Py8 setup used for the comparison with the measurements. The values changed with respect to the CUETP8M2T4 tune are given in the columns corresponding to each model. Further details on parameters or specificities of the models can be looked up in [11,62,3,32,32,4,33,34]. For the rope hadronization model two variations are considered: one with no CR and the other with the default CR model. The settings for the former are denoted in parenthesis in the last column. |
| Summary |
|
The first measurement of the UE activity in $ \mathrm{t\bar{t}} $ dilepton events produced in hadron colliders has been reported, making use of $\sqrt{s} = $ 13 TeV proton-proton collision data acquired by the CMS experiment in 2016. Based on the particle-flow reconstruction [36], the contribution from the underlying event has been isolated by subtracting the charged particles which are associated to the decay products of the $ \mathrm{t\bar{t}} $ event candidates or to pileup events from the set of reconstructed charged particles per event. The chosen observables and categories enhance the sensitivity of the observables to the modeling of multiparton interactions, color reconnection and the choice of ${\alpha_S}^{\rm FSR}$ in the PYTHIA8 parton shower MC. These are among the parameters with largest impact on the modeling of $ \mathrm{t\bar{t}} $ at the LHC. In particular, the compatibility of the data with different choices of the ${\alpha_S}^{FSR}$ parameter in PYTHIA8 has been quantified, resulting in a lower value of ${\alpha_S}^{FSR}$ than Ref. [62]. The majority of the distributions analyzed indicate a fair agreement between the data and the POWHEG+PYTHIA8 setup with the CUETP8M2T4 tune, but disfavor the default settings in HERWIG++, HERWIG7, and SHERPA. It has been furthermore verified that the choice of the NLO matrix-element generator does not impact significantly the expected characteristics of the UE by comparing POWHEG and mg5\_amc@nlo, both interfaced with PYTHIA8. The reported analysis test the universality of the UE hypothesis at higher energy scales than the ones at which the UE models are usually tuned. In addition they can be used to improve the assessment of systematic uncertainties in future top-quark-related analyses. |
| Additional Figures | |
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Additional Figure 1:
Distribution of the p-values corresponding to the observed $\chi ^2$ between the different models and distributions analysed. Each row in the figures corresponds to a different model and the color code represents the number of data distributions for which the model has a given p-value. In all cases the $\chi ^2$ includes only the uncertainty associated to the measurements with the exception of the first row, denoted as Pw+Py8$^*$, which includes also the theory uncertainties associated to the model, as described in the text. The left figure includes the analysis of distributions as function of: $N_{\rm ch}$, $\Sigma p_{\rm T}$, $\Sigma p_{z}$, $\bar{p}_{\rm T}$, $\bar{p}_{z}$ and $|\vec{p}_{\rm T}|$. The right figure includes the analysis of event shape distributions: sphericity, aplanarity, C and D. |
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Additional Figure 1-a:
Distribution of the p-values corresponding to the observed $\chi ^2$ between the different models and distributions analysed. Each row in the figure corresponds to a different model and the color code represents the number of data distributions for which the model has a given p-value. In all cases the $\chi ^2$ includes only the uncertainty associated to the measurements with the exception of the first row, denoted as Pw+Py8$^*$, which includes also the theory uncertainties associated to the model, as described in the text. The figure includes the analysis of distributions as function of: $N_{\rm ch}$, $\Sigma p_{\rm T}$, $\Sigma p_{z}$, $\bar{p}_{\rm T}$, $\bar{p}_{z}$ and $|\vec{p}_{\rm T}|$. |
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Additional Figure 1-b:
Distribution of the p-values corresponding to the observed $\chi ^2$ between the different models and distributions analysed. Each row in the figure corresponds to a different model and the color code represents the number of data distributions for which the model has a given p-value. In all cases the $\chi ^2$ includes only the uncertainty associated to the measurements with the exception of the first row, denoted as Pw+Py8$^*$, which includes also the theory uncertainties associated to the model, as described in the text. The figure includes the analysis of event shape distributions: sphericity, aplanarity, C and D. |
| Additional Tables | |
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Additional Table 1:
The best fit values for $ {\alpha _S} ^{\rm FSR}(m_\mathrm{Z})$ for the Pw+Py8{} setup, obtained from the $\bar{p}_{\rm T}$ distribution in different categories are translated into a preferred value of the renormalization scale ($\mu _{\rm R}$). The $ \pm $1$ \sigma $ interval around a best fit value can be used as an estimate of the variation of the $\mu _{\rm R}$ scale which encompasses the differences between data and the Pw+Py8{} setup. |
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Compact Muon Solenoid LHC, CERN |
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