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arxiv: 2602.11359 · v2 · submitted 2026-02-11 · ⚛️ physics.ins-det · hep-ex

Recognition: 2 theorem links

· Lean Theorem

High-level hadronic tau lepton triggers of the CMS experiment in proton-proton collisions at sqrt{s} = 13.6 TeV

CMS Collaboration
Authors on Pith no claims yet

Pith reviewed 2026-05-16 02:02 UTC · model grok-4.3

classification ⚛️ physics.ins-det hep-ex
keywords CMS triggerhadronic tau leptonmachine learninghigh-level triggertau identificationLHC collisionsproton-proton datatrigger performance
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The pith

Machine-learning algorithms with high efficiency and low cost have been added to the CMS high-level trigger for identifying hadronically decaying tau leptons.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The CMS experiment has updated its trigger system to better capture events with hadronic tau lepton decays amid rising pileup at the LHC. A series of machine-learning algorithms were developed to distinguish genuine tau decays from background jets while keeping computational demands low. These algorithms were tested and their performance measured using proton-proton collision data at 13.6 TeV collected in 2022 and 2023, amounting to 62 fb^{-1}. The work addresses the growing difficulty of tau identification at the trigger level. A sympathetic reader would care because it enables more effective data collection for physics studies that rely on tau leptons.

Core claim

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 trigger performance is summarized using data collected by the CMS experiment in proton-proton collisions at √s = 13.6 TeV in 2022−2023, corresponding to an integrated luminosity of 62 fb^{-1}.

What carries the argument

Machine-learning algorithms for tau identification that operate at the high-level trigger stage to separate genuine tau leptons from quark- and gluon-initiated jets.

If this is right

  • Higher trigger efficiency for processes involving hadronic tau decays.
  • Lower computational cost allows more trigger paths to run simultaneously.
  • Effective discrimination against jets even in high pileup environments.
  • Validated performance metrics drawn directly from 62 fb^{-1} of 2022-2023 collision data.
  • Improved data acquisition rate for tau-based physics measurements and searches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could extend to other trigger objects facing similar identification challenges in future higher-luminosity runs.
  • Wider adoption of low-cost ML at the trigger level may free resources for additional physics channels.
  • Real-data performance validation sets a template for testing similar algorithms in upcoming detector upgrades.
  • Success here could reduce selection biases in tau-related analyses across multiple experiments.

Load-bearing premise

The machine-learning models trained on simulation or limited data will maintain high efficiency and low fake rates when applied to the full range of real collision conditions including varying pileup and detector aging.

What would settle it

Observing a large drop in identification efficiency or a sharp rise in fake rates from jets when the algorithms run on real data, compared to simulation predictions, would falsify the performance claim.

Figures

Figures reproduced from arXiv: 2602.11359 by CMS Collaboration.

Figure 1
Figure 1. Figure 1: Workflows for τh candidate reconstruction at the HLT in Run 2 [21] [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Workflows for τh candidate reconstruction at the HLT in Run 3, since 2022. 5.2 The HPS algorithm The HPS algorithm was first used for the offline reconstruction of hadronic tau lepton decays in Run 2 [18, 40, 41]. It was then introduced online to HLT paths with τh candidates in 2018 to replace the cone-based algorithm [18]. The initial inputs to the algorithm are PF jets with pT > 14 GeV and |η| < 2.5 that… view at source ↗
Figure 3
Figure 3. Figure 3: Performance of the L2TAUNNTAG in the di-τh HLT path, using simulated H → ττ and BSM Z′ → ττ events. The absolute efficiency of the reconstructed L2 τh candidates as a function of the visible generator-level τh pT (left) and η (right) are shown, where “visible” refers to the fact that the contribution of neutrinos is not taken into account. The uncertainties shown by the vertical bars are from the number of… view at source ↗
Figure 4
Figure 4. Figure 4: Performance of the L2TAUNNTAG in the single-τh HLT path, using simulated H → ττ and BSM Z′ → ττ events. The absolute efficiency of the reconstructed L2 τh candidates as a function of the visible generator-level τh pT (left) and η (right) are shown, where “visible” refers to the fact that the contribution of neutrinos is not taken into account. The uncertainties shown by the vertical bars are from the numbe… view at source ↗
Figure 5
Figure 5. Figure 5: Total L1+HLT path efficiency of the eτh (upper left), µτh (upper right), single-τh (lower left), and di-τh (lower right) HLT paths as a function of the visible generator-level τh pT , where “visible” refers to the fact that the contribution of neutrinos is not taken into account. The H → ττ and BSM Z′ → ττ samples are used in the evaluation. The uncertainties shown by the vertical bars are from the number … view at source ↗
Figure 6
Figure 6. Figure 6: A comparison of the L1+HLT efficiency of the eτh monitoring HLT path in 2022 and 2023 as a function of offline τh candidate pT (upper left), η (upper right), and ϕ (lower left). The dependence on the NPV is also shown (lower right). The uncertainties shown by the vertical bars are from the number of events available in the sample, while the horizontal bars show the bin width. Some of the vertical bars are … view at source ↗
Figure 7
Figure 7. Figure 7: A comparison of the L1+HLT efficiency of the µτh HLT path in 2022 and 2023 as a function of offline τh candidate pT (upper left), η (upper right), and ϕ (lower left). The de￾pendence on the NPV is also shown (lower right). The uncertainties shown by the vertical bars are from the number of events available in the sample, while the horizontal bars show the bin width. Some of the vertical bars are smaller th… view at source ↗
Figure 8
Figure 8. Figure 8: A comparison of the L1+HLT efficiency of the di-τh monitoring HLT path in 2022 and 2023 as a function of offline τh candidate pT (upper left), η (upper right), and ϕ (lower left). The dependence on the NPV is also shown (lower right). The uncertainties shown by the vertical bars are from the number of events available in the sample, while the horizontal bars show the bin width. Some of the vertical bars ar… view at source ↗
Figure 9
Figure 9. Figure 9: A comparison of the L1+HLT efficiency of the di-τh+jet monitoring HLT path in 2022 and 2023 as a function of offline τh candidate pT (upper left), η (upper right), and ϕ (lower left). The dependence on the NPV is also shown (lower right). The uncertainties shown by the vertical bars are from the number of events available in the sample, while the horizontal bars show the bin width. Some of the vertical bar… view at source ↗
Figure 10
Figure 10. Figure 10: Efficiencies and scale factors of the HLT monitoring paths using 2022–2023 data as [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
read the original 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 at the trigger level. This is a difficult task, particularly as improvements to the LHC have resulted in an increased number of 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. In this paper, these developments and the trigger performance are summarized using data collected by the CMS experiment in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV in 2022$-$2023, corresponding to an integrated luminosity of 62 fb$^{-1}$.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Circularity Check

0 steps flagged

No significant circularity in experimental performance report

full rationale

This is an experimental paper summarizing the incorporation of machine-learning algorithms into the CMS high-level trigger for hadronic tau leptons and reporting their performance measured directly on real proton-proton collision data from 2022-2023. There is no claimed derivation chain involving predictions or first-principles results that reduce to the inputs by construction. The performance metrics are evaluated on actual data, providing independent validation rather than self-referential fitting.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Standard experimental instrumentation paper; relies on established detector simulation and QCD background modeling without new free parameters or invented entities disclosed in the abstract.

axioms (1)
  • domain assumption Standard Monte Carlo simulation of detector response and pileup accurately models real collision data for trigger efficiency studies
    Invoked implicitly when reporting performance on data after training on simulation

pith-pipeline@v0.9.0 · 5454 in / 1128 out tokens · 80972 ms · 2026-05-16T02:02:55.352841+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

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