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REVIEW 2 major objections 4 minor 33 cited by

Performance of electron and photon triggers in ATLAS during LHC Run 2

T0 review · 2 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read ATLAS electron and photon triggers kept recording physics as LHC luminosity quadrupled in Run 2.

desk verdict ATLAS's full Run 2 electron/photon trigger paper is a solid reference measurement, but the abstract overclaims the diphoton leg efficiency relative to the paper's own per-year numbers. read the letter →

arxiv 1909.00761 v2 pith:33GBXUUY submitted 2019-09-02 hep-ex

ATLAS Collaboration This is my paper · ORCID
classification hep-ex
keywords electrontriggerphotonefficiencyLHCRun2ATLASpile-uptag-and-probeheavy-ioncollisions
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper establishes that the ATLAS electron and photon triggers recorded the physics events they were designed for even as LHC collision rates and pile-up roughly quadrupled between 2015 and 2018. It reports data-driven efficiencies measured with the full Run 2 dataset: a single-electron trigger catches at least 75% of offline-selected 31 GeV electrons and 96% at 60 GeV, and the 25 GeV leg of the primary diphoton trigger passes more than 96% of tight 30 GeV offline photons. The same is shown to hold for heavy-ion collisions once the underlying-event background is subtracted online. Because analyses depend on these trigger efficiencies for nearly every electron and photon measurement, the paper supplies the corrections and systematic uncertainties that make those measurements possible.

What carries the argument

The measurement rests on two data-driven efficiency methods plus the two-level trigger chain. Electron efficiencies come from tag-and-probe: $Z\to ee$ events supply a sample of unbiased probe electrons whose trigger decision is known, with backgrounds subtracted using the $Z$-mass distribution. Photon efficiencies use the Bootstrap method, which compares HLT acceptance on a low-threshold triggered sample with a random-trigger sample of tight offline photons, cross-checked against a very pure sample from radiative $Z\to \ell\ell\gamma$ decays. The trigger itself is a two-stage system: a hardware Level-1 calorimeter selection, then a software high-level trigger with fast calorimeter and tracking steps followed by precision reconstruction and likelihood-based identification. For electrons above 15 GeV, the fast step uses the Ringer neural-network algorithm, which encodes calorimeter energy into concentric rings and cut the CPU cost of the lowest-threshold single-electron trigger by at least 50%.

What would settle it

Measure the same trigger leg in data with the high-purity radiative $Z\to\ell\ell\gamma$ method and with the Bootstrap method in the same $E_{\mathrm{T}}$ bins just above threshold; if the two differ by more than the assigned systematic uncertainty, the Bootstrap bias assumption is falsified. As a second check, if the data-to-simulation efficiency ratios for tight photons deviate from unity by more than the quoted uncertainties in a full-simulation $H\to\gamma\gamma$ sample, the simulation-faithfulness assumption fails.

Watch

Extended reading notes

Core claim

The central claim is that the trigger menus were re-optimized year by year so that a fourfold luminosity increase and average pile-up near 60 interactions per crossing did not come at the price of trigger efficiency. In proton-proton collisions, the single-electron trigger combination is at least 75% efficient relative to a tight offline electron selection at $E_{\mathrm{T}}=31$ GeV and rises to 96% at 60 GeV; the 25 GeV leg of the primary diphoton trigger is more than 96% efficient for a tight, isolated offline photon at $E_{\mathrm{T}}=30$ GeV. In heavy-ion data, the primary electron and photon triggers are at least 84% and 95% efficient, respectively, at 5 GeV above threshold. The measured data-to-simulation efficiency correction factors stay below about 4% above 40 GeV in most of the detector, so analyses can apply them with small residual uncertainty.

Load-bearing premise

For the Bootstrap photon efficiency, the paper assumes that background photons passing the tight offline identification are caught by the trigger almost as often as genuine signal photons, so the low-purity Bootstrap sample is only mildly biased.

Editorial extensions

If this is right

  • Analyses using a single electron above $E_{\mathrm{T}}=60$ GeV can treat the trigger as essentially fully efficient, with data-to-simulation corrections below 4% above 40 GeV.
  • The primary diphoton trigger supports Higgs-boson analyses down to 30 GeV photons with more than 96% per-leg efficiency, so the trigger adds little to the offline acceptance loss.
  • The measured efficiency correction factors, typically known to about 0.1%, allow simulation-based analyses to model the trigger without large systematic penalties.
  • Heavy-ion analyses can trigger on electrons and photons at 15-20 GeV thresholds with centrality-independent efficiency once the underlying-event subtraction is applied online.
  • The Ringer algorithm reduced the CPU demand of the lowest-threshold single-electron trigger by at least 50%, freeing high-level-trigger resources for the rest of the menu.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The flat efficiency up to $\langle\mu\rangle\sim 60$ suggests the same trigger architecture has headroom for the higher luminosities of later LHC runs, provided the isolation and identification thresholds are periodically retuned.
  • The Bootstrap bias from background photons, which the paper argues is small, could be probed directly by extending the radiative-$Z$ method to higher $E_{\mathrm{T}}$, where it currently does not reach.
  • The success of per-cell underlying-event subtraction for heavy-ion photon triggers suggests the same technique could push heavy-ion trigger thresholds below 15 GeV in future runs.
  • Because the Ringer algorithm was trained on 2017 data for 2018 running, periodic retraining on the most recent collision data may become a standard operational step in later data-taking periods.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. This paper reports the performance of the ATLAS electron and photon triggers during LHC Run 2 (2015–2018) proton-proton and heavy-ion data-taking. It describes the evolution of trigger thresholds, identification working points, and isolation requirements, and it presents measured trigger efficiencies as functions of offline electron/photon ET, pseudorapidity, and pile-up, using data-driven tag-and-probe, bootstrap, and radiative-Z methods. The headline results include a single-electron trigger efficiency of at least 75% at 31 GeV rising to 96% at 60 GeV, a claim that the 25 GeV leg of the primary diphoton trigger is more than 96% efficient for a 30 GeV tight isolated photon, and heavy-ion efficiencies of at least 84% (electrons) and 95% (photons) at 5 GeV above threshold.

Significance. The paper provides reference trigger performance numbers that are used by ATLAS physics analyses, and it documents the operational choices (e.g., the move to medium photon identification, the Ringer algorithm, L1 isolation changes) made to control rates under increasing pile-up. Its strengths include per-year efficiency measurements with statistical and systematic uncertainties, a cross-check of the two independent photon efficiency methods in Figure 4, data/MC ratios that validate the systematic procedure, and a detailed breakdown of electron trigger inefficiency sources in Table 9. If the headline diphoton efficiency claim is corrected, the paper will be a standard, useful reference for the community.

major comments (2)
  1. [Abstract and Section 13 vs Section 9.2, Figure 9] The abstract and conclusion state that the 25 GeV leg of the primary diphoton trigger is 'more than 96%' efficient for an offline photon of 30 GeV with tight identification and isolation. However, Section 9.2 states that for 2017–2018, when the online 'medium' identification WP was used, the leg is '~95% efficient for events with offline tight isolated photons with ET at least 5 GeV above the trigger threshold'. For the 25 GeV leg, 'at least 5 GeV above threshold' includes ET = 30 GeV, so the two statements are inconsistent unless the >96% value refers only to the 2015–2016 loose-WP period, which is not stated. This is a load-bearing discrepancy in the paper's central summary claim and must be resolved by year-by-year qualification or correction of the quoted number.
  2. [Table 6 vs Section 9.2] There is an internal inconsistency about when the medium identification WP was introduced for the primary diphoton trigger. Table 6 lists g35_medium_g25_medium as the primary diphoton trigger already for 2016, while Section 9.2 says 'During 2015 and 2016 loose identification was used at the HLT for primary diphoton triggers. During 2017–2018, medium identification was used'. If 2016 already used medium, the sentence in Section 9.2 assigning the ~95% efficiency to a 2017–2018 tightening is wrong; if 2016 still used loose, Table 6 is wrong. This must be corrected because it affects the interpretation of Figure 9 and the quoted per-year efficiencies.
minor comments (4)
  1. [Section 7.3] The text contains a spacing typo and an unclear phrase: 'a fewGeVabove the trigger threshold' should be 'a few GeV above the trigger threshold', and the sentence structure could be improved for readability.
  2. [Reference [43]] Reference [43] is titled 'Neutral Networks and Learning Machines'; the correct title is 'Neural Networks and Learning Machines'.
  3. [Figure 6 caption] The figure shows statistical uncertainties only and notes that background subtraction is omitted; this is acceptable for monitoring plots, but the text should explicitly state that the quoted efficiency values in Section 8 are not used for physics corrections, to avoid any impression that the L1 efficiencies carry full systematic uncertainties.
  4. [Figure 4 caption] The caption lists the trigger names 'g25_medium g35_medium' without a separator; adding a comma ('g25_medium, g35_medium') would avoid ambiguity.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the trigger efficiencies are data-driven measurements relative to independent offline selections, and cited prior work is methodological rather than load-bearing.

full rationale

The paper's central claims are measurements, not derivations. The electron trigger efficiency is measured with the tag-and-probe method (Section 7.2), where the denominator is an offline electron selection and the numerator is the trigger decision; the trigger is not used to define the offline selection, so there is no self-definitional loop. The photon efficiency uses the Bootstrap and radiative-Z methods (Section 7.3). The factorization epsilon_trig = epsilon_HLT|BS * epsilon_BS is a definitional product of conditional efficiencies, not an equivalence between input and output. The Bootstrap sample is collected with L1-only, loose low-ET, or random triggers rather than the trigger under study, and the assumption that background photons passing tight offline identification have trigger efficiency close to signal photons is explicitly stated and covered by an assigned systematic uncertainty (Section 7.3). Self-citations (Refs. [3, 31, 41, 46]) provide reconstruction algorithms, identification working points, and previously established measurement techniques; they are not used as the evidence for the efficiency values reported here and do not forbid alternative approaches. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no ansatz is smuggled in via citation. The abstract's unqualified 'more than 96%' statement for the 25 GeV diphoton leg conflicts with the per-year '~95%' statement in Section 9.2 for 2017-2018 and with the timing of the loose-to-medium identification change in Table 6, but that is an internal reporting inconsistency, not circularity. The derivation chain is therefore self-contained relative to the offline definitions and data-taking conditions.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters are fit to produce the central efficiencies; the measured efficiencies and their systematic uncertainties are reported directly from data. Thresholds and identification cuts are operational choices, not parameters of the claim. No new entities are introduced.

assumptions (3)
  • domain assumption The tag-and-probe method selects an unbiased sample of probe electrons from Z to ee decays using strict tag requirements.
    Section 7.2 uses Z to ee decays and assumes the tag side does not bias the probe and residual background is handled by the Zmass method.
  • domain assumption Monte Carlo simulation reproduces the data trigger efficiency up to small correction factors.
    Systematic uncertainties are estimated from data/MC discrepancies in Sections 7.2 and 7.3, and correction factors rely on this.
  • domain assumption Background photons in the Bootstrap sample have trigger efficiency close to signal photons.
    Section 7.3 uses this to argue biases in the low-purity Bootstrap sample are small.

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Cite this review

Pith. "Pith review of Performance of electron and photon triggers in ATLAS during LHC Run 2." pith.science (2026). https://pith.science/paper/33GBXUUY

@misc{pith2026190900761,
  author       = {Pith},
  title        = {Pith review of: Performance of electron and photon triggers in ATLAS during LHC Run 2},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/33GBXUUY}},
  note         = {Machine review of arXiv:1909.00761}
}
abstract

Electron and photon triggers covering transverse energies from 5 GeV to several TeV are essential for the ATLAS experiment to record signals for a wide variety of physics: from Standard Model processes to searches for new phenomena in both proton-proton and heavy-ion collisions. To cope with a fourfold increase of peak LHC luminosity from 2015 to 2018 (Run 2), to 2.1$\times$10$^{34}$cm$^{-2}$s$^{-1}$, and a similar increase in the number of interactions per beam-crossing to about 60, trigger algorithms and selections were optimised to control the rates while retaining a high efficiency for physics analyses. For proton-proton collisions, the single-electron trigger efficiency relative to a single-electron offline selection is at least 75% for an offline electron of 31 GeV, and rises to 96% at 60 GeV; the trigger efficiency of a 25 GeV leg of the primary diphoton trigger relative to a tight offline photon selection is more than 96% for an offline photon of 30 GeV. For heavy-ion collisions, the primary electron and photon trigger efficiencies relative to the corresponding standard offline selections are at least 84% and 95%, respectively, at 5 GeV above the corresponding trigger threshold.

Discussion (0). Continue with ORCID to comment.

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