CMS-MUO-22-001 ; CERN-EP-2023-205 | ||
Muon identification using multivariate techniques in the CMS experiment in proton-proton collisions at $ \sqrt{s}= $ 13 TeV | ||
CMS Collaboration | ||
5 October 2023 | ||
JINST 19 (2024) P02031 | ||
Abstract: The identification of prompt and isolated muons, as well as muons from heavy-flavour hadron decays, is an important task. We developed two multivariate techniques to provide highly efficient identification for muons with transverse momentum greater than 10 GeV. One provides a continuous variable as an alternative to a cut-based identification selection and offers a better discrimination power against misidentified muons. The other one selects prompt and isolated muons by using isolation requirements to reduce the contamination from nonprompt muons arising in heavy-flavour hadron decays. Both algorithms are developed using 59.7 fb$ ^{-1} $ of proton-proton collisions data at a centre-of-mass energy of $ \sqrt{s}= $ 13 TeV collected in 2018 with the CMS experiment at the CERN LHC. | ||
Links: e-print arXiv:2310.03844 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; Physics Briefing ; CADI line (restricted) ; |
Figures | |
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Figure 1:
Composition of the simulated $ \mathrm{t\bar{t}} $ sample used for training after muon preselection in terms of muon origin according to generator-level information. The composition is shown as a function of $ p_{\mathrm{T}} $ (left) and $ \eta $ (right). The last bin on the left figure includes events with $ p_{\mathrm{T}} > $ 100 GeV. |
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Figure 1-a:
Composition of the simulated $ \mathrm{t\bar{t}} $ sample used for training after muon preselection in terms of muon origin according to generator-level information. The composition is shown as a function of $ p_{\mathrm{T}} $. The last bin includes events with $ p_{\mathrm{T}} > $ 100 GeV. |
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Figure 1-b:
Composition of the simulated $ \mathrm{t\bar{t}} $ sample used for training after muon preselection in terms of muon origin according to generator-level information. The composition is shown as a function of $ \eta $. |
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Figure 2:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the number of matched stations (upper left), the segment compatibility (upper central), the number of tracker layers with hits (upper right), the fraction of valid tracker hits (middle left), the inner-standalone matching (middle central) and normalized $ \chi^2 $ of the muon fit (middle right), the number of valid pixel hits (lower left) and the total number of valid muon chamber hits (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), shown for signal and background, as defined in Section 5.1. The last bin of each distribution contains the overflow events. |
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Figure 2-a:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the number of matched stations, shown for signal and background, as defined in Section 5.1. The last bin contains the overflow events. |
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Figure 2-b:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the segment compatibility, shown for signal and background, as defined in Section 5.1. The last bin contains the overflow events. |
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Figure 2-c:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the number of tracker layers with hits, shown for signal and background, as defined in Section 5.1. The last bin contains the overflow events. |
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Figure 2-d:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the fraction of valid tracker hits, shown for signal and background, as defined in Section 5.1. |
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Figure 2-e:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the inner-standalone matching, shown for signal and background, as defined in Section 5.1. The last bin contains the overflow events. |
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Figure 2-f:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the normalized $ \chi^2 $ of the muon fit, shown for signal and background, as defined in Section 5.1. The last bin contains the overflow events. |
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Figure 2-g:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the number of valid pixel hits, shown for signal and background, as defined in Section 5.1. The last bin contains the overflow events. |
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Figure 2-h:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the total number of valid muon chamber hits, shown for signal and background, as defined in Section 5.1. The last bin contains the overflow events. |
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Figure 2-i:
Distribution in the simulated $ \mathrm{t\bar{t}} $ training sample of the $ \chi^2 $ of the kink-finder algorithm, shown for signal and background, as defined in Section 5.1. The last bin contains the overflow events. |
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Figure 3:
The ROC curve for muons with $ p_{\mathrm{T}} > $ 10 GeV for the developed general muon MVA ID discriminator (black solid line) with the selected medium and tight WPs shown as orange solid and purple open stars, respectively. Orange solid and blue open points show the medium and tight WPs of the cut-based ID. The ROC curve of the soft MVA ID [11] is also shown (grey dashed line). |
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Figure 4:
Simulated distributions of the charged component (upper left) and the neutral component (upper central) of mini-isolation, the muon to jet $ p_{\mathrm{T}} $ ratio (upper right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated DEEPJET B tagging algorithm (middle central), the significance of the impact parameter (middle right), the impact parameter in the transverse (lower left) and longitudinal (lower central) direction between the muon and the PV, and the segment compatibility (lower right) for prompt and nonprompt muons. |
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Figure 4-a:
Simulated distribution of the charged component of mini-isolation, for prompt and nonprompt muons. |
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Figure 4-b:
Simulated distribution of the neutral component of mini-isolation, for prompt and nonprompt muons. |
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Figure 4-c:
Simulated distribution of the muon to jet $ p_{\mathrm{T}} $ ratio, for prompt and nonprompt muons. |
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Figure 4-d:
Simulated distribution of the jet relative $ p_{\mathrm{T}} $, for prompt and nonprompt muons. |
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Figure 4-e:
Simulated distribution of the score of the associated DEEPJET B tagging algorithm, for prompt and nonprompt muons. |
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Figure 4-f:
Simulated distribution of the significance of the impact parameter, for prompt and nonprompt muons. |
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Figure 4-g:
Simulated distribution of the impact parameter in the transverse longitudinal direction between the muon and the PV, for prompt and nonprompt muons. |
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Figure 4-h:
Simulated distribution of the segment compatibility, for prompt and nonprompt muons. |
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Figure 4-i:
Simulated distribution of the segment compatibility, for prompt and nonprompt muons. |
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Figure 5:
ROC curve for the prompt-muon MVA, and for the tight and medium cut-based criteria together with requirements on mini-isolation. For a set of cuts on the different discriminators, the efficiency is shown as a function of proportion of nonprompt muons passing the WP selection. |
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Figure 6:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. The efficiencies of the muon MVA ID are similar in both $ \eta $ regions. |
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Figure 6-a:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9. Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. The efficiencies of the muon MVA ID are similar in both $ \eta $ regions. |
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Figure 6-b:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| > $ 0.9. Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. The efficiencies of the muon MVA ID are similar in both $ \eta $ regions. |
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Figure 6-c:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. The efficiencies of the muon MVA ID are similar in both $ \eta $ regions. |
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Figure 6-d:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. The efficiencies of the muon MVA ID are similar in both $ \eta $ regions. |
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Figure 7:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. |
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Figure 7-a:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. |
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Figure 7-b:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. |
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Figure 7-c:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. |
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Figure 7-d:
Muon identification efficiency for the medium (upper) and tight (lower) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the muon MVA ID performance both for the 2018 data set and DY simulation, whereas red triangles show the efficiency of the medium cut-based ID used during Run 2. The data to MC ratio is also shown. |
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Figure 8:
Efficiency of the prompt-muon MVA selection as a function of the muon $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right), for the 2018 data set with black dots and simulated DY events with red triangles. The vertical bars on the plots represent the statistical uncertainty of each measurement. |
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Figure 8-a:
Efficiency of the prompt-muon MVA selection as a function of the muon $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right), for the 2018 data set with black dots and simulated DY events with red triangles. The vertical bars on the plots represent the statistical uncertainty of each measurement. |
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Figure 8-b:
Efficiency of the prompt-muon MVA selection as a function of the muon $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right), for the 2018 data set with black dots and simulated DY events with red triangles. The vertical bars on the plots represent the statistical uncertainty of each measurement. |
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Figure 9:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper central), segment compatibility (upper right), the DEEPJET B tagging score of the jet associated to the muon (lower left), the significance of the impact parameter (lower central) and the prompt-muon MVA score (lower right). The vertical bars on the dots represent the statistical uncertainty of each data point and the blue band, the uncertainty associated to the limited number of simulated events. |
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Figure 9-a:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper central), segment compatibility (upper right), the DEEPJET B tagging score of the jet associated to the muon (lower left), the significance of the impact parameter (lower central) and the prompt-muon MVA score (lower right). The vertical bars on the dots represent the statistical uncertainty of each data point and the blue band, the uncertainty associated to the limited number of simulated events. |
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Figure 9-b:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper central), segment compatibility (upper right), the DEEPJET B tagging score of the jet associated to the muon (lower left), the significance of the impact parameter (lower central) and the prompt-muon MVA score (lower right). The vertical bars on the dots represent the statistical uncertainty of each data point and the blue band, the uncertainty associated to the limited number of simulated events. |
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Figure 9-c:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper central), segment compatibility (upper right), the DEEPJET B tagging score of the jet associated to the muon (lower left), the significance of the impact parameter (lower central) and the prompt-muon MVA score (lower right). The vertical bars on the dots represent the statistical uncertainty of each data point and the blue band, the uncertainty associated to the limited number of simulated events. |
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Figure 9-d:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper central), segment compatibility (upper right), the DEEPJET B tagging score of the jet associated to the muon (lower left), the significance of the impact parameter (lower central) and the prompt-muon MVA score (lower right). The vertical bars on the dots represent the statistical uncertainty of each data point and the blue band, the uncertainty associated to the limited number of simulated events. |
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Figure 9-e:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper central), segment compatibility (upper right), the DEEPJET B tagging score of the jet associated to the muon (lower left), the significance of the impact parameter (lower central) and the prompt-muon MVA score (lower right). The vertical bars on the dots represent the statistical uncertainty of each data point and the blue band, the uncertainty associated to the limited number of simulated events. |
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Figure 9-f:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper central), segment compatibility (upper right), the DEEPJET B tagging score of the jet associated to the muon (lower left), the significance of the impact parameter (lower central) and the prompt-muon MVA score (lower right). The vertical bars on the dots represent the statistical uncertainty of each data point and the blue band, the uncertainty associated to the limited number of simulated events. |
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Figure 10:
Measurement of the nonprompt-muon rate of a prompt-muon MVA (blue dots) and mini-isolation (red triangles) selection as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 1.2 (left) and $ |\eta| > $ 1.2 (right) for the 2018 data set and simulated DY events. |
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Figure 10-a:
Measurement of the nonprompt-muon rate of a prompt-muon MVA (blue dots) and mini-isolation (red triangles) selection as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 1.2 for the 2018 data set and simulated DY events. |
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Figure 10-b:
Measurement of the nonprompt-muon rate of a prompt-muon MVA (blue dots) and mini-isolation (red triangles) selection as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| > $ 1.2 for the 2018 data set and simulated DY events. |
Summary |
A correct identification of the leptons is crucial in many precision measurements and searches to suppress the otherwise overwhelming background and as an indicator of interesting physical processes. Two multivariate analyses were developed for highly efficient muon identification and isolation. The first one, the muon MVA ID, is trained to distinguish muons produced promptly in heavy gauge boson decays as well as muons from $ \tau $ lepton and heavy-flavour hadron decays, from background muons produced in light-hadron decays (pions or kaons) or other spurious signatures in the detector that could be misreconstructed as muons. The discriminator is presented as an alternative to the standard cut-based identification criteria and could be used for high-efficiency working points. The second one, the prompt-muon MVA, selects isolated muons from W, Z, Higgs bosons, and $ \tau $ lepton decays to reduce the contamination from nonisolated muons arising in heavy-flavour hadron decays. Their performances are measured in proton-proton collisions recorded by the CMS experiment during 2018 and compared to simulation. The performance of the muon MVA ID improves significantly that of the cut-based ID and the prompt-muon MVA achieves a factor 2--3 times smaller nonprompt-muon rates than the mini-isolation selection. |
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