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| CMS-PAS-TAU-24-002 | ||
| Performance of the high-level hadronic $ \tau $ triggers of the CMS experiment in proton-proton collisions at $ \sqrt{s} = $ 13.6 TeV | ||
| CMS Collaboration | ||
| 2025-10-20 | ||
| Abstract: The trigger system of the CMS detector is pivotal in the acquisition of data for physics measurements and searches. Studies of final states characterized by hadronic decays of tau leptons require the reconstruction and the identification of genuine tau leptons against quark- and gluon-initiated jets in the trigger system. This is a difficult task, particularly as improvements to the LHC have resulted in more interactions per bunch crossing in recent years. To address this challenge, a series of machine learning algorithms with high identification efficiency and low computational cost have been incorporated into the high-level trigger for hadronically decaying tau leptons: the L2TauNNTag and the online version of DeepTau. In this note, these developments and the trigger performance are summarized using the data collected by the CMS experiment in proton-proton collisions at $ \sqrt{s}= $ 13.6 TeV from the years 2022 and 2023, corresponding to an integrated luminosity of 62 fb$ ^{-1} $. | ||
| Links: CDS record (PDF) ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Workflows for $ \tau_\mathrm{h} $ candidate reconstruction at the HLT in Run 2 [42]. |
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Figure 2:
Workflows for $ \tau_\mathrm{h} $ candidate reconstruction at the HLT in Run 3, since 2022. |
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Figure 3:
Performance of the L2TAUNNTAG in the di-$ \tau_\mathrm{h} $ HLT path, using simulated VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ events. The absolute efficiency of the reconstructed L2 $ \tau_\mathrm{h} $ candidates as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $ (left) and $ \eta $ (right) are shown, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 3-a:
Performance of the L2TAUNNTAG in the di-$ \tau_\mathrm{h} $ HLT path, using simulated VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ events. The absolute efficiency of the reconstructed L2 $ \tau_\mathrm{h} $ candidates as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $ (left) and $ \eta $ (right) are shown, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 3-b:
Performance of the L2TAUNNTAG in the di-$ \tau_\mathrm{h} $ HLT path, using simulated VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ events. The absolute efficiency of the reconstructed L2 $ \tau_\mathrm{h} $ candidates as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $ (left) and $ \eta $ (right) are shown, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 4:
Performance of the L2TAUNNTAG in the single-$ \tau_\mathrm{h} $ HLT path, using simulated VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ events. The absolute efficiency of the reconstructed L2 $ \tau_\mathrm{h} $ candidates as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $ (left) and $ \eta $ (right) are shown, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 4-a:
Performance of the L2TAUNNTAG in the single-$ \tau_\mathrm{h} $ HLT path, using simulated VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ events. The absolute efficiency of the reconstructed L2 $ \tau_\mathrm{h} $ candidates as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $ (left) and $ \eta $ (right) are shown, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 4-b:
Performance of the L2TAUNNTAG in the single-$ \tau_\mathrm{h} $ HLT path, using simulated VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ events. The absolute efficiency of the reconstructed L2 $ \tau_\mathrm{h} $ candidates as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $ (left) and $ \eta $ (right) are shown, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 5:
Total L1+HLT path efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ (upper left), $ \mu\tau_\mathrm{h} $ (upper right), single-$ \tau_\mathrm{h} $ (lower left), di-$ \tau_\mathrm{h} $ (lower right) HLT paths as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ samples are used in the evaluation. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 5-a:
Total L1+HLT path efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ (upper left), $ \mu\tau_\mathrm{h} $ (upper right), single-$ \tau_\mathrm{h} $ (lower left), di-$ \tau_\mathrm{h} $ (lower right) HLT paths as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ samples are used in the evaluation. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 5-b:
Total L1+HLT path efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ (upper left), $ \mu\tau_\mathrm{h} $ (upper right), single-$ \tau_\mathrm{h} $ (lower left), di-$ \tau_\mathrm{h} $ (lower right) HLT paths as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ samples are used in the evaluation. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 5-c:
Total L1+HLT path efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ (upper left), $ \mu\tau_\mathrm{h} $ (upper right), single-$ \tau_\mathrm{h} $ (lower left), di-$ \tau_\mathrm{h} $ (lower right) HLT paths as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ samples are used in the evaluation. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 5-d:
Total L1+HLT path efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ (upper left), $ \mu\tau_\mathrm{h} $ (upper right), single-$ \tau_\mathrm{h} $ (lower left), di-$ \tau_\mathrm{h} $ (lower right) HLT paths as a function of the visible generator-level $ \tau_\mathrm{h} p_{\mathrm{T}} $, where ``visible'' refers to the fact that the contribution of neutrinos is not taken into account. The VBF $ \mathrm{H}\to\tau\tau $ and BSM $ \mathrm{Z}^{'}\to\tau\tau $ samples are used in the evaluation. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 6:
A comparison of the L1+HLT efficiency of the $ \mu\tau_\mathrm{h} $ HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 6-a:
A comparison of the L1+HLT efficiency of the $ \mu\tau_\mathrm{h} $ HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 6-b:
A comparison of the L1+HLT efficiency of the $ \mu\tau_\mathrm{h} $ HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 6-c:
A comparison of the L1+HLT efficiency of the $ \mu\tau_\mathrm{h} $ HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 6-d:
A comparison of the L1+HLT efficiency of the $ \mu\tau_\mathrm{h} $ HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 7:
A comparison of the L1+HLT efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 7-a:
A comparison of the L1+HLT efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 7-b:
A comparison of the L1+HLT efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 7-c:
A comparison of the L1+HLT efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
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Figure 7-d:
A comparison of the L1+HLT efficiency of the $ \mathrm{e}\tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 8:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 8-a:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 8-b:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 8-c:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 8-d:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 9:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} + \text{jet} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 9-a:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} + \text{jet} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 9-b:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} + \text{jet} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 9-c:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} + \text{jet} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 9-d:
A comparison of the L1+HLT efficiency of the di-$ \tau_\mathrm{h} + \text{jet} $ monitoring HLT path in 2022 and 2023 as a function of offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ (upper left), $ \eta $ (upper right), and $ \phi $ (lower left). The dependence on the number of primary vertices is also shown (lower right). The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. |
png pdf |
Figure 10:
Efficiencies and scale factors of the HLT monitoring paths using 2022 and 2023 data as a function of the offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ for the $ \mu\tau_\mathrm{h} $ (upper left), $ \mathrm{e}\tau_\mathrm{h} $ (upper right), di-$ \tau_\mathrm{h} $ (lower left), and di-$ \tau_\mathrm{h} $ + jet (lower right) HLT paths. The measured efficiencies for data are shown with black markers, and for simulation with green markers. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. The corresponding dotted lines of the same color display the best fit results together with the statistical error bands. The scale factors, defined as ratios of efficiencies between data and simulation, are displayed in the bottom panel with an associated purple error band. Only values to the right of the red dotted line are used, to avoid large statistical fluctuations, as they are sufficiently above the turn-on threshold of the trigger. |
png pdf |
Figure 10-a:
Efficiencies and scale factors of the HLT monitoring paths using 2022 and 2023 data as a function of the offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ for the $ \mu\tau_\mathrm{h} $ (upper left), $ \mathrm{e}\tau_\mathrm{h} $ (upper right), di-$ \tau_\mathrm{h} $ (lower left), and di-$ \tau_\mathrm{h} $ + jet (lower right) HLT paths. The measured efficiencies for data are shown with black markers, and for simulation with green markers. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. The corresponding dotted lines of the same color display the best fit results together with the statistical error bands. The scale factors, defined as ratios of efficiencies between data and simulation, are displayed in the bottom panel with an associated purple error band. Only values to the right of the red dotted line are used, to avoid large statistical fluctuations, as they are sufficiently above the turn-on threshold of the trigger. |
png pdf |
Figure 10-b:
Efficiencies and scale factors of the HLT monitoring paths using 2022 and 2023 data as a function of the offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ for the $ \mu\tau_\mathrm{h} $ (upper left), $ \mathrm{e}\tau_\mathrm{h} $ (upper right), di-$ \tau_\mathrm{h} $ (lower left), and di-$ \tau_\mathrm{h} $ + jet (lower right) HLT paths. The measured efficiencies for data are shown with black markers, and for simulation with green markers. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. The corresponding dotted lines of the same color display the best fit results together with the statistical error bands. The scale factors, defined as ratios of efficiencies between data and simulation, are displayed in the bottom panel with an associated purple error band. Only values to the right of the red dotted line are used, to avoid large statistical fluctuations, as they are sufficiently above the turn-on threshold of the trigger. |
png pdf |
Figure 10-c:
Efficiencies and scale factors of the HLT monitoring paths using 2022 and 2023 data as a function of the offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ for the $ \mu\tau_\mathrm{h} $ (upper left), $ \mathrm{e}\tau_\mathrm{h} $ (upper right), di-$ \tau_\mathrm{h} $ (lower left), and di-$ \tau_\mathrm{h} $ + jet (lower right) HLT paths. The measured efficiencies for data are shown with black markers, and for simulation with green markers. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. The corresponding dotted lines of the same color display the best fit results together with the statistical error bands. The scale factors, defined as ratios of efficiencies between data and simulation, are displayed in the bottom panel with an associated purple error band. Only values to the right of the red dotted line are used, to avoid large statistical fluctuations, as they are sufficiently above the turn-on threshold of the trigger. |
png pdf |
Figure 10-d:
Efficiencies and scale factors of the HLT monitoring paths using 2022 and 2023 data as a function of the offline $ \tau_\mathrm{h} $ candidate $ p_{\mathrm{T}} $ for the $ \mu\tau_\mathrm{h} $ (upper left), $ \mathrm{e}\tau_\mathrm{h} $ (upper right), di-$ \tau_\mathrm{h} $ (lower left), and di-$ \tau_\mathrm{h} $ + jet (lower right) HLT paths. The measured efficiencies for data are shown with black markers, and for simulation with green markers. The uncertainties shown by the error bars are from the number of events available in the sample. Some of the error bars are smaller than the markers and are not shown. The corresponding dotted lines of the same color display the best fit results together with the statistical error bands. The scale factors, defined as ratios of efficiencies between data and simulation, are displayed in the bottom panel with an associated purple error band. Only values to the right of the red dotted line are used, to avoid large statistical fluctuations, as they are sufficiently above the turn-on threshold of the trigger. |
| Tables | |
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Table 1:
Decay modes and branching fractions ($ \mathcal{B} $) of the tau lepton alongside the mesonic resonances primarily involved in hadronic tau lepton decays [36]. |
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Table 2:
The performance between cut based and L2TAUNNTAG. The first column is the expected rate of the Run 2 cut-based paths linearly scaled to Run 3 conditions. The second column is the rate of L2TAUNNTAG paths estimated with Run 2 data and scaled to Run 3 conditions. The third column is the rate of L2TAUNNTAG paths evaluated in real Run 3 condition. The Run 2 estimation is based on an instantaneous luminosity of 1.68 $ \times $ 10$^{34}$ cm$^{-2}$s$^{-1} $. The Run 3 projection is evaluated with 2.0 $ \times $ 10$^{34}$ cm$^{-2}$s$^{-1} $. The Run 3 evaluation is performed with 2.2 $ \times $ 10$^{34}$ cm$^{-2}$s$^{-1} $. The rates are inclusive calculations not excluding shared contributions from other algorithms or paths. |
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Table 3:
Rate estimation and observation for several $ \tau_\mathrm{h} $ HLT paths. The first column is the expected rate of the Run 2 cut-based paths linearly scaled to Run 3 luminosity. The second column is the rate of DEEPTAU paths estimated with Run 2 data and scaled to Run 3 luminosity. The third column is the rate of DEEPTAU paths observed in Run 3. The Run 2 estimation is based on an instantaneous luminosity of 1.68 $ \times $ 10$^{34}$ cm$^{-2}$s$^{-1} $. The Run 3 projection is evaluated with 2.0 $ \times $ 10$^{34}$ cm$^{-2}$s$^{-1} $. The Run 3 observed rates are recorded with 1.87 $ \times $ 10$^{34}$ cm$^{-2}$s$^{-1} $. The rates are inclusive calculations not excluding shared contributions from other algorithms or paths. |
| Summary |
| Two machine learning algorithms, the L2TAUNNTAG and online DEEPTAU algorithms, have been described and incorporated into the high level trigger (HLT) for hadronically decaying tau lepton ($ \tau_\mathrm{h} $) candidates. Their performance has been evaluated using the data collected by the CMS experiment in proton-proton collisions at $ \sqrt{s} = $ 13.6 TeV from the years 2022 and 2023, corresponding to an integrated luminosity of 62 fb$ ^{-1} $. Comparisons to simulation were performed and show good agreement with the collected data, validating the current understanding of the HLT paths involving $ \tau_\mathrm{h} $ candidates. The updated HLT paths are found to deliver improved $ \tau_\mathrm{h} $ candidate identification efficiency without significantly increasing computational cost or event rate, allowing more genuine hadronic tau lepton decays to be collected at roughly the same resource cost as in 2018. These improvements will benefit physics studies targeting final states with hadronically decaying tau leptons, including precision measurements of the Higgs boson, and searches beyond the standard model. |
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