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| CMS-TAU-24-001 ; CERN-EP-2025-233 | ||
| Identification of tau leptons using a convolutional neural network with domain adaptation | ||
| CMS Collaboration | ||
| 7 November 2025 | ||
| Accepted for publication in J. Instrum. | ||
| Abstract: A tau lepton identification algorithm, DEEPTAU, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons ($ \tau_\mathrm{h} $) from quark or gluon jets and electrons and muons that are misreconstructed as $ \tau_\mathrm{h} $ candidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine $ \tau_\mathrm{h} $ candidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30-50% in the probability for quark and gluon jets to be misidentified as $ \tau_\mathrm{h} $ candidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in the DEEPTAU algorithm and evaluates its performance in LHC proton-proton collision data at $ \sqrt{s}= $ 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb$ ^{-1} $, respectively. Techniques to calibrate the performance of the $ \tau_\mathrm{h} $ identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses. | ||
| Links: e-print arXiv:2511.05468 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Schematic illustration of the signatures of the $ \mathrm{h}^{\pm} $, $ \mathrm{h}^{\pm}\pi^{0} $, $ \mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm} $, and $ \mathrm{h}^{\pm}\mathrm{h}^{\mp}\mathrm{h}^{\pm}\pi^{0} $ decay modes of the tau lepton in the CMS detector. Charged hadrons are reconstructed by the PF algorithm by matching tracks with energy deposits in the ECAL and HCAL, whereas the HPS algorithm aims to reconstruct each $ \pi^{0}\to\gamma\gamma $ decay as a single ``strip'' of energy clusters in ECAL. |
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Figure 2:
Inner and outer grid layout in $ \eta$-$\phi $ space [21]. The inner grid encapsulates the signal cone of maximal radius 0.1, which contains the $ \mathrm{h}^{\pm} $ and $ \pi^{0} $ candidates, and consists of 11 $ {\times} $ 11 cells with a size of 0.02 $ {\times} $ 0.02 each. The outer grid contains the isolation cone of radius 0.5, and consists of 21 $ {\times} $ 21 cells with a size of 0.05 $ {\times} $ 0.05 each. |
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Figure 3:
The DEEPTAU architecture with the domain adaptation configuration [66]. A set of final domain adaptation layers was introduced for data-simulation discrimination, consisting of several dense layers followed by a softmax layer that yields an output $ y_\text{adv} $ between zero and one. The backpropagation is modified to include the ``adversarial loss'', as described in the text. |
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Figure 4:
Distribution of the DEEPTAU discriminator against quark and gluon jets before (left) and after (right) domain adaptation, for the 2018 dataset used for domain adaptation training. There is significant improvement in data-simulation agreement in the control region, with the discrepancies in the final bin reduced to 0.9%. The vertical bars on the data points indicate the statistical uncertainty; on most points the bars are smaller than the marker size. |
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Figure 4-a:
Distribution of the DEEPTAU discriminator against quark and gluon jets before (left) and after (right) domain adaptation, for the 2018 dataset used for domain adaptation training. There is significant improvement in data-simulation agreement in the control region, with the discrepancies in the final bin reduced to 0.9%. The vertical bars on the data points indicate the statistical uncertainty; on most points the bars are smaller than the marker size. |
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Figure 4-b:
Distribution of the DEEPTAU discriminator against quark and gluon jets before (left) and after (right) domain adaptation, for the 2018 dataset used for domain adaptation training. There is significant improvement in data-simulation agreement in the control region, with the discrepancies in the final bin reduced to 0.9%. The vertical bars on the data points indicate the statistical uncertainty; on most points the bars are smaller than the marker size. |
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Figure 5:
Distribution of the DEEPTAU discriminator against quark and gluon jets before (left) and after (right) domain adaptation, for the early 2022 dataset. While data-to-simulation differences remain, there is an appreciable improvement in the final bins with the inclusion of domain adaptation, despite DEEPTAU being trained on 2018 data and simulation. The vertical bars on the data points indicate the statistical uncertainty; on most points the bars are smaller than the marker size. |
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Figure 5-a:
Distribution of the DEEPTAU discriminator against quark and gluon jets before (left) and after (right) domain adaptation, for the early 2022 dataset. While data-to-simulation differences remain, there is an appreciable improvement in the final bins with the inclusion of domain adaptation, despite DEEPTAU being trained on 2018 data and simulation. The vertical bars on the data points indicate the statistical uncertainty; on most points the bars are smaller than the marker size. |
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Figure 5-b:
Distribution of the DEEPTAU discriminator against quark and gluon jets before (left) and after (right) domain adaptation, for the early 2022 dataset. While data-to-simulation differences remain, there is an appreciable improvement in the final bins with the inclusion of domain adaptation, despite DEEPTAU being trained on 2018 data and simulation. The vertical bars on the data points indicate the statistical uncertainty; on most points the bars are smaller than the marker size. |
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Figure 6:
Jet misidentification probability versus genuine $ \tau_\mathrm{h} $ identification efficiency for low-$ p_{\mathrm{T}} $ (left) and high-$ p_{\mathrm{T}} $ (right) $ \tau_\mathrm{h} $ candidates, evaluated on 2018 simulated datasets. The genuine $ \tau_\mathrm{h} $ identification efficiency is estimated from $ \mathrm{H}\to \tau \tau $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that match generator-level $ \tau_\mathrm{h} $ objects. The jet misidentification probability is estimated from $ \mathrm{t} \overline{\mathrm{t}} $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that do not match prompt electrons, muons, or products of $ \tau_\mathrm{h} $ decays at the generator level. The defined working points of the discriminator are indicated as filled circles. |
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Figure 6-a:
Jet misidentification probability versus genuine $ \tau_\mathrm{h} $ identification efficiency for low-$ p_{\mathrm{T}} $ (left) and high-$ p_{\mathrm{T}} $ (right) $ \tau_\mathrm{h} $ candidates, evaluated on 2018 simulated datasets. The genuine $ \tau_\mathrm{h} $ identification efficiency is estimated from $ \mathrm{H}\to \tau \tau $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that match generator-level $ \tau_\mathrm{h} $ objects. The jet misidentification probability is estimated from $ \mathrm{t} \overline{\mathrm{t}} $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that do not match prompt electrons, muons, or products of $ \tau_\mathrm{h} $ decays at the generator level. The defined working points of the discriminator are indicated as filled circles. |
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Figure 6-b:
Jet misidentification probability versus genuine $ \tau_\mathrm{h} $ identification efficiency for low-$ p_{\mathrm{T}} $ (left) and high-$ p_{\mathrm{T}} $ (right) $ \tau_\mathrm{h} $ candidates, evaluated on 2018 simulated datasets. The genuine $ \tau_\mathrm{h} $ identification efficiency is estimated from $ \mathrm{H}\to \tau \tau $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that match generator-level $ \tau_\mathrm{h} $ objects. The jet misidentification probability is estimated from $ \mathrm{t} \overline{\mathrm{t}} $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that do not match prompt electrons, muons, or products of $ \tau_\mathrm{h} $ decays at the generator level. The defined working points of the discriminator are indicated as filled circles. |
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Figure 7:
Electron misidentification probability versus genuine $ \tau_\mathrm{h} $ identification efficiency for low-$ p_{\mathrm{T}} $ (left) and high-$ p_{\mathrm{T}} $ (right) $ \tau_\mathrm{h} $ candidates, evaluated on 2018 simulated datasets. The genuine $ \tau_\mathrm{h} $ identification efficiency is estimated from $ \mathrm{H} \to \tau\tau $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that match generator-level $ \tau_\mathrm{h} $ objects. The electron misidentification probability is estimated from $ \mathrm{Z}/\gamma^{*}+$jets simulation using reconstructed $ \tau_\mathrm{h} $ candidates that match electrons at the generator level. The defined working points of the discriminator are indicated as filled circles. |
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Figure 7-a:
Electron misidentification probability versus genuine $ \tau_\mathrm{h} $ identification efficiency for low-$ p_{\mathrm{T}} $ (left) and high-$ p_{\mathrm{T}} $ (right) $ \tau_\mathrm{h} $ candidates, evaluated on 2018 simulated datasets. The genuine $ \tau_\mathrm{h} $ identification efficiency is estimated from $ \mathrm{H} \to \tau\tau $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that match generator-level $ \tau_\mathrm{h} $ objects. The electron misidentification probability is estimated from $ \mathrm{Z}/\gamma^{*}+$jets simulation using reconstructed $ \tau_\mathrm{h} $ candidates that match electrons at the generator level. The defined working points of the discriminator are indicated as filled circles. |
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Figure 7-b:
Electron misidentification probability versus genuine $ \tau_\mathrm{h} $ identification efficiency for low-$ p_{\mathrm{T}} $ (left) and high-$ p_{\mathrm{T}} $ (right) $ \tau_\mathrm{h} $ candidates, evaluated on 2018 simulated datasets. The genuine $ \tau_\mathrm{h} $ identification efficiency is estimated from $ \mathrm{H} \to \tau\tau $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that match generator-level $ \tau_\mathrm{h} $ objects. The electron misidentification probability is estimated from $ \mathrm{Z}/\gamma^{*}+$jets simulation using reconstructed $ \tau_\mathrm{h} $ candidates that match electrons at the generator level. The defined working points of the discriminator are indicated as filled circles. |
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Figure 8:
Muon misidentification probability versus $ \tau_\mathrm{h} $ identification efficiency for low-$ p_{\mathrm{T}} $ (left) and high-$ p_{\mathrm{T}} $ (right) $ \tau_\mathrm{h} $ candidates, evaluated on simulated 2018 datasets. The $ \tau_\mathrm{h} $ identification efficiency is estimated from $ \mathrm{H} \to \tau\tau $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that match generator-level $ \tau_\mathrm{h} $ objects. The muon misidentification probability is estimated from $ \mathrm{Z}/\gamma^{*}+$jets simulation using reconstructed $ \tau_\mathrm{h} $ candidates that match muons at the generator level. The defined working points of the discriminator are indicated as filled circles. |
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Figure 8-a:
Muon misidentification probability versus $ \tau_\mathrm{h} $ identification efficiency for low-$ p_{\mathrm{T}} $ (left) and high-$ p_{\mathrm{T}} $ (right) $ \tau_\mathrm{h} $ candidates, evaluated on simulated 2018 datasets. The $ \tau_\mathrm{h} $ identification efficiency is estimated from $ \mathrm{H} \to \tau\tau $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that match generator-level $ \tau_\mathrm{h} $ objects. The muon misidentification probability is estimated from $ \mathrm{Z}/\gamma^{*}+$jets simulation using reconstructed $ \tau_\mathrm{h} $ candidates that match muons at the generator level. The defined working points of the discriminator are indicated as filled circles. |
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Figure 8-b:
Muon misidentification probability versus $ \tau_\mathrm{h} $ identification efficiency for low-$ p_{\mathrm{T}} $ (left) and high-$ p_{\mathrm{T}} $ (right) $ \tau_\mathrm{h} $ candidates, evaluated on simulated 2018 datasets. The $ \tau_\mathrm{h} $ identification efficiency is estimated from $ \mathrm{H} \to \tau\tau $ simulations using reconstructed $ \tau_\mathrm{h} $ candidates that match generator-level $ \tau_\mathrm{h} $ objects. The muon misidentification probability is estimated from $ \mathrm{Z}/\gamma^{*}+$jets simulation using reconstructed $ \tau_\mathrm{h} $ candidates that match muons at the generator level. The defined working points of the discriminator are indicated as filled circles. |
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Figure 9:
Distribution of the invariant mass of the reconstructed $ \mu\,\tau_\mathrm{h} $ system when using DEEPTAU v2.1 (left) and v2.5 (right) for discrimination in the 2018 dataset. The DEEPTAU working points used are: Medium for $ D_\text{jet} $, VVLoose for $ D_\mathrm{e} $ and, Tight for $ D_\mu $ (see Table 2). The correction factors are applied in both cases. The vertical bars correspond to the statistical uncertainties in the observed event yields. |
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Figure 9-a:
Distribution of the invariant mass of the reconstructed $ \mu\,\tau_\mathrm{h} $ system when using DEEPTAU v2.1 (left) and v2.5 (right) for discrimination in the 2018 dataset. The DEEPTAU working points used are: Medium for $ D_\text{jet} $, VVLoose for $ D_\mathrm{e} $ and, Tight for $ D_\mu $ (see Table 2). The correction factors are applied in both cases. The vertical bars correspond to the statistical uncertainties in the observed event yields. |
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Figure 9-b:
Distribution of the invariant mass of the reconstructed $ \mu\,\tau_\mathrm{h} $ system when using DEEPTAU v2.1 (left) and v2.5 (right) for discrimination in the 2018 dataset. The DEEPTAU working points used are: Medium for $ D_\text{jet} $, VVLoose for $ D_\mathrm{e} $ and, Tight for $ D_\mu $ (see Table 2). The correction factors are applied in both cases. The vertical bars correspond to the statistical uncertainties in the observed event yields. |
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Figure 10:
The data-to-simulation scale factors of the $ \tau_\mathrm{h} $ identification efficiency as functions of $ p_{\mathrm{T}} $ in the 2018 (left) and 2022 (right) data-taking periods, including all $ \tau_\mathrm{h} $ decay modes, and requiring the $ D_\text{jet} $ Medium working point (see Table 2) and $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) < $ 65 GeV. The vertical bars correspond to the combined statistical and systematic uncertainties in the individual scale factors. For a fair scale factor comparison in 2022, the tau energy scale have been fixed to the one measured for DEEPTAU v2.5 which showcases higher genuine $ \tau_\mathrm{h} $ purity. |
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Figure 10-a:
The data-to-simulation scale factors of the $ \tau_\mathrm{h} $ identification efficiency as functions of $ p_{\mathrm{T}} $ in the 2018 (left) and 2022 (right) data-taking periods, including all $ \tau_\mathrm{h} $ decay modes, and requiring the $ D_\text{jet} $ Medium working point (see Table 2) and $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) < $ 65 GeV. The vertical bars correspond to the combined statistical and systematic uncertainties in the individual scale factors. For a fair scale factor comparison in 2022, the tau energy scale have been fixed to the one measured for DEEPTAU v2.5 which showcases higher genuine $ \tau_\mathrm{h} $ purity. |
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Figure 10-b:
The data-to-simulation scale factors of the $ \tau_\mathrm{h} $ identification efficiency as functions of $ p_{\mathrm{T}} $ in the 2018 (left) and 2022 (right) data-taking periods, including all $ \tau_\mathrm{h} $ decay modes, and requiring the $ D_\text{jet} $ Medium working point (see Table 2) and $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) < $ 65 GeV. The vertical bars correspond to the combined statistical and systematic uncertainties in the individual scale factors. For a fair scale factor comparison in 2022, the tau energy scale have been fixed to the one measured for DEEPTAU v2.5 which showcases higher genuine $ \tau_\mathrm{h} $ purity. |
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Figure 11:
The $ m_\text{vis} $ distribution in the $ \mathrm{Z}\to\tau_\mu\tau_\mathrm{h} $ channel for the 2022 dataset before (left) and after (right) the full calibration. The DEEPTAU working points used are: Medium for $ D_\text{jet} $, VVLoose for $ D_\mathrm{e} $ and, Tight for $ D_\mu $ (see Table 2). The application of correction factors improves the agreement between data and simulation. |
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Figure 11-a:
The $ m_\text{vis} $ distribution in the $ \mathrm{Z}\to\tau_\mu\tau_\mathrm{h} $ channel for the 2022 dataset before (left) and after (right) the full calibration. The DEEPTAU working points used are: Medium for $ D_\text{jet} $, VVLoose for $ D_\mathrm{e} $ and, Tight for $ D_\mu $ (see Table 2). The application of correction factors improves the agreement between data and simulation. |
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Figure 11-b:
The $ m_\text{vis} $ distribution in the $ \mathrm{Z}\to\tau_\mu\tau_\mathrm{h} $ channel for the 2022 dataset before (left) and after (right) the full calibration. The DEEPTAU working points used are: Medium for $ D_\text{jet} $, VVLoose for $ D_\mathrm{e} $ and, Tight for $ D_\mu $ (see Table 2). The application of correction factors improves the agreement between data and simulation. |
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Figure 12:
The $ m_\text{vis} $ distribution in the $ \mathrm{Z}\to\tau_\mathrm{e}\tau_\mathrm{h} $ channel for the 2022 dataset before (left) and after (right) the full calibration. The DEEPTAU working points used are: Medium for $ D_\text{jet} $, Tight for $ D_\mathrm{e} $ and, Tight for $ D_\mu $ (see Table 2). Specific 2022 detector conditions that affected electron reconstruction are not perfectly modelled in the simulation. As a result, the amount of electrons misidentified as $ \tau_\mathrm{h} $ is enhanced in data with respect to simulated events. The application of correction factors improves the agreement between data and simulation. |
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Figure 12-a:
The $ m_\text{vis} $ distribution in the $ \mathrm{Z}\to\tau_\mathrm{e}\tau_\mathrm{h} $ channel for the 2022 dataset before (left) and after (right) the full calibration. The DEEPTAU working points used are: Medium for $ D_\text{jet} $, Tight for $ D_\mathrm{e} $ and, Tight for $ D_\mu $ (see Table 2). Specific 2022 detector conditions that affected electron reconstruction are not perfectly modelled in the simulation. As a result, the amount of electrons misidentified as $ \tau_\mathrm{h} $ is enhanced in data with respect to simulated events. The application of correction factors improves the agreement between data and simulation. |
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Figure 12-b:
The $ m_\text{vis} $ distribution in the $ \mathrm{Z}\to\tau_\mathrm{e}\tau_\mathrm{h} $ channel for the 2022 dataset before (left) and after (right) the full calibration. The DEEPTAU working points used are: Medium for $ D_\text{jet} $, Tight for $ D_\mathrm{e} $ and, Tight for $ D_\mu $ (see Table 2). Specific 2022 detector conditions that affected electron reconstruction are not perfectly modelled in the simulation. As a result, the amount of electrons misidentified as $ \tau_\mathrm{h} $ is enhanced in data with respect to simulated events. The application of correction factors improves the agreement between data and simulation. |
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Figure 13:
Summary of $ \tau_\mathrm{h} $ identification efficiency (left) and $ \tau_\mathrm{h} $ energy scale corrections (right) across the $ \tau_\mathrm{h} $ decay modes and $ p_{\mathrm{T}} $ regions for 2018 with $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) < $ 65 GeV and the $ D_\text{jet} $ Medium working point (see Table 2). The horizontal bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 13-a:
Summary of $ \tau_\mathrm{h} $ identification efficiency (left) and $ \tau_\mathrm{h} $ energy scale corrections (right) across the $ \tau_\mathrm{h} $ decay modes and $ p_{\mathrm{T}} $ regions for 2018 with $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) < $ 65 GeV and the $ D_\text{jet} $ Medium working point (see Table 2). The horizontal bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 13-b:
Summary of $ \tau_\mathrm{h} $ identification efficiency (left) and $ \tau_\mathrm{h} $ energy scale corrections (right) across the $ \tau_\mathrm{h} $ decay modes and $ p_{\mathrm{T}} $ regions for 2018 with $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) < $ 65 GeV and the $ D_\text{jet} $ Medium working point (see Table 2). The horizontal bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 14:
Summary of $ \tau_\mathrm{h} $ identification efficiency (left) and $ \tau_\mathrm{h} $ energy scale corrections (right) across the $ \tau_\mathrm{h} $ decay modes for 2022 with $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) < $ 40 GeV and $ D_\text{jet} $ Medium working point (see Table 2). The horizontal bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 14-a:
Summary of $ \tau_\mathrm{h} $ identification efficiency (left) and $ \tau_\mathrm{h} $ energy scale corrections (right) across the $ \tau_\mathrm{h} $ decay modes for 2022 with $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) < $ 40 GeV and $ D_\text{jet} $ Medium working point (see Table 2). The horizontal bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 14-b:
Summary of $ \tau_\mathrm{h} $ identification efficiency (left) and $ \tau_\mathrm{h} $ energy scale corrections (right) across the $ \tau_\mathrm{h} $ decay modes for 2022 with $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) < $ 40 GeV and $ D_\text{jet} $ Medium working point (see Table 2). The horizontal bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 15:
Muon misidentification rate scale factors binned by the $ \tau_\mathrm{h} |\eta| $ for the Medium $ D_\mu $ working point (see Table 2). Measurement for the 2018 dataset is shown on the left and for the 2022 dataset on the right. The dashed lines indicate the boundaries of the $ \tau_\mathrm{h} |\eta| $ bins. The vertical bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 15-a:
Muon misidentification rate scale factors binned by the $ \tau_\mathrm{h} |\eta| $ for the Medium $ D_\mu $ working point (see Table 2). Measurement for the 2018 dataset is shown on the left and for the 2022 dataset on the right. The dashed lines indicate the boundaries of the $ \tau_\mathrm{h} |\eta| $ bins. The vertical bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 15-b:
Muon misidentification rate scale factors binned by the $ \tau_\mathrm{h} |\eta| $ for the Medium $ D_\mu $ working point (see Table 2). Measurement for the 2018 dataset is shown on the left and for the 2022 dataset on the right. The dashed lines indicate the boundaries of the $ \tau_\mathrm{h} |\eta| $ bins. The vertical bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 16:
Summary plots of results for electron misidentification rate scale factors divided in decay modes and $ \eta $ regions for the VVLoose $ D_\mathrm{e} $ working point (see Table 2). The corrections are shown for the 2018 (left) and 2022 (right) datasets. The horizontal bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 16-a:
Summary plots of results for electron misidentification rate scale factors divided in decay modes and $ \eta $ regions for the VVLoose $ D_\mathrm{e} $ working point (see Table 2). The corrections are shown for the 2018 (left) and 2022 (right) datasets. The horizontal bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 16-b:
Summary plots of results for electron misidentification rate scale factors divided in decay modes and $ \eta $ regions for the VVLoose $ D_\mathrm{e} $ working point (see Table 2). The corrections are shown for the 2018 (left) and 2022 (right) datasets. The horizontal bars represent the total uncertainty on the measurements, combining both statistical and systematic contributions. |
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Figure 17:
The high-$ p_{\mathrm{T}} \tau_\mathrm{h} $ identification efficiency scale factors as a function of $ \tau_\mathrm{h} p_{\mathrm{T}} $ for $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ VVLoose (left) and Tight (right) discriminators (see Table 2). The scale factors are measured for the 2018 (top) and 2022 (bottom) datasets. |
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Figure 17-a:
The high-$ p_{\mathrm{T}} \tau_\mathrm{h} $ identification efficiency scale factors as a function of $ \tau_\mathrm{h} p_{\mathrm{T}} $ for $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ VVLoose (left) and Tight (right) discriminators (see Table 2). The scale factors are measured for the 2018 (top) and 2022 (bottom) datasets. |
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Figure 17-b:
The high-$ p_{\mathrm{T}} \tau_\mathrm{h} $ identification efficiency scale factors as a function of $ \tau_\mathrm{h} p_{\mathrm{T}} $ for $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ VVLoose (left) and Tight (right) discriminators (see Table 2). The scale factors are measured for the 2018 (top) and 2022 (bottom) datasets. |
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Figure 17-c:
The high-$ p_{\mathrm{T}} \tau_\mathrm{h} $ identification efficiency scale factors as a function of $ \tau_\mathrm{h} p_{\mathrm{T}} $ for $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ VVLoose (left) and Tight (right) discriminators (see Table 2). The scale factors are measured for the 2018 (top) and 2022 (bottom) datasets. |
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Figure 17-d:
The high-$ p_{\mathrm{T}} \tau_\mathrm{h} $ identification efficiency scale factors as a function of $ \tau_\mathrm{h} p_{\mathrm{T}} $ for $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ VVLoose (left) and Tight (right) discriminators (see Table 2). The scale factors are measured for the 2018 (top) and 2022 (bottom) datasets. |
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Figure 18:
Prefit (left plots) and postfit (right plots) distribution of $ m_{\mathrm{T}}(p_{\mathrm{T}}^{\tau_\mathrm{h}}, p_{\mathrm{T}}^\text{miss}) $ for $ p_{\mathrm{T}} $ bins of 100 $ < p_{\mathrm{T}} < $ 200 GeV (upper plots) and $ p_{\mathrm{T}} > $ 200 GeV (lower plots) in the 2022 dataset. Distributions are obtained for a combination of $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ Tight discriminators (see Table 2). |
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Figure 18-a:
Prefit (left plots) and postfit (right plots) distribution of $ m_{\mathrm{T}}(p_{\mathrm{T}}^{\tau_\mathrm{h}}, p_{\mathrm{T}}^\text{miss}) $ for $ p_{\mathrm{T}} $ bins of 100 $ < p_{\mathrm{T}} < $ 200 GeV (upper plots) and $ p_{\mathrm{T}} > $ 200 GeV (lower plots) in the 2022 dataset. Distributions are obtained for a combination of $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ Tight discriminators (see Table 2). |
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Figure 18-b:
Prefit (left plots) and postfit (right plots) distribution of $ m_{\mathrm{T}}(p_{\mathrm{T}}^{\tau_\mathrm{h}}, p_{\mathrm{T}}^\text{miss}) $ for $ p_{\mathrm{T}} $ bins of 100 $ < p_{\mathrm{T}} < $ 200 GeV (upper plots) and $ p_{\mathrm{T}} > $ 200 GeV (lower plots) in the 2022 dataset. Distributions are obtained for a combination of $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ Tight discriminators (see Table 2). |
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Figure 18-c:
Prefit (left plots) and postfit (right plots) distribution of $ m_{\mathrm{T}}(p_{\mathrm{T}}^{\tau_\mathrm{h}}, p_{\mathrm{T}}^\text{miss}) $ for $ p_{\mathrm{T}} $ bins of 100 $ < p_{\mathrm{T}} < $ 200 GeV (upper plots) and $ p_{\mathrm{T}} > $ 200 GeV (lower plots) in the 2022 dataset. Distributions are obtained for a combination of $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ Tight discriminators (see Table 2). |
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Figure 18-d:
Prefit (left plots) and postfit (right plots) distribution of $ m_{\mathrm{T}}(p_{\mathrm{T}}^{\tau_\mathrm{h}}, p_{\mathrm{T}}^\text{miss}) $ for $ p_{\mathrm{T}} $ bins of 100 $ < p_{\mathrm{T}} < $ 200 GeV (upper plots) and $ p_{\mathrm{T}} > $ 200 GeV (lower plots) in the 2022 dataset. Distributions are obtained for a combination of $ D_\text{jet} $ Medium, $ D_\mu $ Tight and $ D_\mathrm{e} $ Tight discriminators (see Table 2). |
| Tables | |
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Table 1:
Selection requirements for the domain adaptation dataset. The impact parameters for the muon (or $ \tau_\mathrm{h} $ candidate), $ d_z $ and $ d_{xy} $, are defined as the distances between the muon track (or leading charged-hadron track) and the PV. The medium muon identification is defined in Ref. [27]. The previous DEEPTAU discriminator scores described in Ref. [21] against quark and gluon jets, electrons, and muons, are denoted $ D_\text{jet}^\text{v2.1} $, $ D_\mathrm{e}^\text{v2.1} $, and $ D_\mu^\text{v2.1} $. The transverse mass of the muon $ p_{\mathrm{T}} $ and the missing transverse momentum system is denoted as $ m_{\mathrm{T}}(p_{\mathrm{T}}^\mu,p_{\mathrm{T}}^\text{miss}) $. Working points Tight and VVLoose are defined in Table 2. |
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Table 2:
Target genuine $ \tau_\mathrm{h} $ identification efficiencies for the different working points defined for the three discriminators. The target efficiencies are evaluated with the $ \mathrm{H}\to\tau\tau $ event sample for $ \tau_\mathrm{h} $ candidates with $ p_{\mathrm{T}} \in $ [30, 70] GeV. |
png pdf |
Table A1:
Default values of the parameters used in the classification loss function for DEEPTAU training. |
| Summary |
| In this paper, the newly deployed version of the DEEPTAU algorithm, v2.5, used to discriminate $ \tau_\mathrm{h} $ candidates from quark or gluon jets and electrons and muons, has been introduced. This deep convolutional neural network exhibits improved performance with respect to its predecessor, reducing the jet misidentification rate by 30-50% for a given $ \tau_\mathrm{h} $ reconstruction and identification efficiency. The implementation of domain adaptation by backpropagation has reduced performance discrepancies between collision data and simulation, decreasing the necessary residual corrections by 5%. The domain adaptation was introduced by including an adversarial subnetwork in the gradient calculation of the neural network. This adversarial subnetwork was designed to discriminate between collision data and simulations, running in parallel with the $ \tau_\mathrm{h} $ classification task. The DEEPTAU algorithm, trained using both collision data and simulated samples, is able to maximize the $ \tau_\mathrm{h} $ classification performance, while minimizing the data-simulation discrepancies. The DEEPTAU v2.5 algorithm was trained on simulated proton-proton collision data corresponding to the 2018 data-taking conditions, as well as on real collision data collected during the same year containing $ \mathrm{Z} \to \tau\tau $ decays, which was used for domain adaptation. The algorithm has been validated using 2018 and 2022 collision data. The observed $ \tau_\mathrm{h} $ efficiencies were found to agree with the expected efficiencies from simulated events within 10% for 2018 and 15% for 2022. This agreement is improved with respect to the previous iteration of the algorithm and confirms the effectiveness of domain adaptation. The algorithm has been introduced to be used in CMS physics analyses using data recorded from 2022 onwards. |
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