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| CMS-PAS-MUO-22-001 | ||
| Identification of prompt and isolated muons using multivariate techniques at the CMS experiment in proton-proton collisions at $ \sqrt{s}= $ 13 TeV | ||
| CMS Collaboration | ||
| 22 May 2023 | ||
| Abstract: Prompt and isolated muons as well as muons from heavy flavour decays represent a key object for many analyses at CMS either to select the signal final states or to reject the background events. In this note we present two multivariate techniques that have been developed to provide a highly efficient identification algorithm for muons with transverse momentum greater than 10 GeV. One has been trained as an alternative to the standard cut-based identification criteria but with higher efficiency working points, and offers a continuous variable which provides more flexibility to pick the desired working points. The second one aims to select 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 data produced in proton-proton collisions at a center-of-mass energy of $ \sqrt{s}= $ 13 TeV collected during 2018 with the CMS experiment at CERN LHC. Their performance has been assessed in both data and simulation. The measured efficiencies for the first MVA are similar or better than those achieved by the standard cut-based selection criteria. While the second MVA is key to reduce background contribution from nonprompt muons, which leads to an increase in sensitivity crucial both in precision standard model measurements as well as in beyond standard model searches. | ||
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Links:
CDS record (PDF) ;
CADI line (restricted) ;
These preliminary results are superseded in this paper, Accepted by JINST. The superseded preliminary plots can be found here. |
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| Figures | Summary | Additional Figures | References | CMS Publications |
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| Figures | |
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Figure 1:
Composition of the $ \mathrm{t} \bar{\mathrm{t}} $ sample used for training after muon preselection in terms of the muon origing acording to generator information. The composition is shown as a function of $ p_{\mathrm{T}} $ (left) and $ \eta $ (right). |
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Figure 1-a:
Composition of the $ \mathrm{t} \bar{\mathrm{t}} $ sample used for training after muon preselection in terms of the muon origing acording to generator information. The composition is shown as a function of $ p_{\mathrm{T}} $ (left) and $ \eta $ (right). |
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Figure 1-b:
Composition of the $ \mathrm{t} \bar{\mathrm{t}} $ sample used for training after muon preselection in terms of the muon origing acording to generator information. The composition is shown as a function of $ p_{\mathrm{T}} $ (left) and $ \eta $ (right). |
png pdf |
Figure 2:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
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Figure 2-a:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
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Figure 2-b:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
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Figure 2-c:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
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Figure 2-d:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
png pdf |
Figure 2-e:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
png pdf |
Figure 2-f:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
png pdf |
Figure 2-g:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
png pdf |
Figure 2-h:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
png pdf |
Figure 2-i:
Distribution in the $ \mathrm{t} \bar{\mathrm{t}} $ training sample of the number of matched stations (top 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 hits in the muon detectors (lower central), and the $ \chi^2 $ of the kink-finder algorithm (lower right), divided in signal and background. Signal and background are defined in 5.1. All variables are plotted after the preselection described in Section 5.1. |
png pdf |
Figure 3:
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 and purple stars, respectively. Orange and blue points show the medium and tight WPs of the cut-based ID. The ROC curve of the soft MVA is shown in gray. |
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Figure 4:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
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Figure 4-a:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
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Figure 4-b:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
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Figure 4-c:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
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Figure 4-d:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
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Figure 4-e:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
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Figure 4-f:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
png pdf |
Figure 4-g:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
png pdf |
Figure 4-h:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
png pdf |
Figure 4-i:
Distribution of the charged component (top left) and the neutral component (top central) of mini-isolation, the jet $ p_{\mathrm{T}} $ ratio (top right), the jet relative $ p_{\mathrm{T}} $ (middle left), the score of the associated deep flavor (middle center), the significance of the impact parameter (middle right), the impact parameter in the transverse (bottom left) and longitudinal (bottom center) direction between the muon and the PV, and the segment compatibility (bottom right). |
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Figure 5:
ROC curve for the developed prompt muon MVA discriminator together with requirements on mini-isolation and the tight and medium cut-based criteria. For each working point, the efficiency is shown as a function of proportion of nonprompt muons passing the working point selection. |
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Figure 6:
Muon identification efficiency for the medium (top) and tight (bottom) 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 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 6-a:
Muon identification efficiency for the medium (top) and tight (bottom) 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 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 6-b:
Muon identification efficiency for the medium (top) and tight (bottom) 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 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 6-c:
Muon identification efficiency for the medium (top) and tight (bottom) 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 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 6-d:
Muon identification efficiency for the medium (top) and tight (bottom) 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 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 7:
Muon identification efficiency for the medium (top) and tight (bottom) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the MVA ID performance both for 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 7-a:
Muon identification efficiency for the medium (top) and tight (bottom) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the MVA ID performance both for 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 7-b:
Muon identification efficiency for the medium (top) and tight (bottom) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the MVA ID performance both for 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 7-c:
Muon identification efficiency for the medium (top) and tight (bottom) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the MVA ID performance both for 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 7-d:
Muon identification efficiency for the medium (top) and tight (bottom) WPs as a function of PU for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right). Blue dots show the MVA ID performance both for 2018 dataset and DY while red triangles show the efficiency of the cut-based ID used during Run 2. |
png pdf |
Figure 8:
Efficiency of the prompt MVA selection as a function of the muon $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right), for 2018 dataset in black and DY events in red. |
png pdf |
Figure 8-a:
Efficiency of the prompt MVA selection as a function of the muon $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right), for 2018 dataset in black and DY events in red. |
png pdf |
Figure 8-b:
Efficiency of the prompt MVA selection as a function of the muon $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 0.9 (left) and $ |\eta| > $ 0.9 (right), for 2018 dataset in black and DY events in red. |
png pdf |
Figure 9:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (top left), $ \eta $ (top right), segment compatibility (middle left), the deep flavor b-tagging of the jet associated to the muon (middle right), the significance of the impact parameter (bottom left) and the prompt MVA score (bottom right). |
png pdf |
Figure 9-a:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (top left), $ \eta $ (top right), segment compatibility (middle left), the deep flavor b-tagging of the jet associated to the muon (middle right), the significance of the impact parameter (bottom left) and the prompt MVA score (bottom right). |
png pdf |
Figure 9-b:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (top left), $ \eta $ (top right), segment compatibility (middle left), the deep flavor b-tagging of the jet associated to the muon (middle right), the significance of the impact parameter (bottom left) and the prompt MVA score (bottom right). |
png pdf |
Figure 9-c:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (top left), $ \eta $ (top right), segment compatibility (middle left), the deep flavor b-tagging of the jet associated to the muon (middle right), the significance of the impact parameter (bottom left) and the prompt MVA score (bottom right). |
png pdf |
Figure 9-d:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (top left), $ \eta $ (top right), segment compatibility (middle left), the deep flavor b-tagging of the jet associated to the muon (middle right), the significance of the impact parameter (bottom left) and the prompt MVA score (bottom right). |
png pdf |
Figure 9-e:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (top left), $ \eta $ (top right), segment compatibility (middle left), the deep flavor b-tagging of the jet associated to the muon (middle right), the significance of the impact parameter (bottom left) and the prompt MVA score (bottom right). |
png pdf |
Figure 9-f:
Distribution of data in the multijet control region as a function of the muon $ p_{\mathrm{T}} $ (top left), $ \eta $ (top right), segment compatibility (middle left), the deep flavor b-tagging of the jet associated to the muon (middle right), the significance of the impact parameter (bottom left) and the prompt MVA score (bottom right). |
png pdf |
Figure 10:
Measurement of the muon nonprompt rate of a prompt MVA (red) and mini-isolation (blue) selection as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 1.2 (left) and $ |\eta| > $ 1.2 (right) for the 2018 dataset and DY events. |
png pdf |
Figure 10-a:
Measurement of the muon nonprompt rate of a prompt MVA (red) and mini-isolation (blue) selection as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 1.2 (left) and $ |\eta| > $ 1.2 (right) for the 2018 dataset and DY events. |
png pdf |
Figure 10-b:
Measurement of the muon nonprompt rate of a prompt MVA (red) and mini-isolation (blue) selection as a function of $ p_{\mathrm{T}} $ for muons with $ |\eta| < $ 1.2 (left) and $ |\eta| > $ 1.2 (right) for the 2018 dataset and DY events. |
| Summary |
| The presence of leptons is crucial in many precision measurements and searches for suppressing the otherwise overwhelming background and as an indicator of interesting physical processes. Two multivariate (MVA) techniques have been developed for a highly efficient muon identification and isolation. One, the MVA ID, has been 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 MVA, aims to select isolated muons from W, Z, H bosons, and $ \tau $ lepton decays to reduce contamination from nonisolated muons arising in heavy flavour hadron decays. Their performance has been measured in proton-proton collisions recorded by the CMS experiment during 2018 and compared to simulation performance. The measured efficiencies are similar to or better than those achieved by the standard cut-based selection criteria. |
| Additional Figures | |
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Additional Figure 1:
ROC curve for muons with $ p_{\mathrm{T}} > $ 10 GeV for the developed general muon MVA ID discriminator (black solid line). Orange and blue points show the medium and tight WPs of the cut-based ID. The ROC curve of the soft MVA is also shown (gray dashed line). |
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