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arxiv: 2505.15951 · v4 · submitted 2025-05-21 · ✦ hep-ex

A framework and implementation for data-driven trigger efficiency estimation at LHCb

Johannes Albrecht , James Andrew Gooding , Maxim Lysenko , Abhijit Mathad , Alessandro Scarabotto , Tomasz Skwarnicki This is my paper

Pith reviewed 2026-05-22 13:37 UTC · model grok-4.3

classification ✦ hep-ex
keywords LHCbtrigger efficiencydata-drivenTriggerCalibparticle physicssoftware packageuncertainty estimationreconstructed candidates
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The pith

A data-driven method estimates trigger efficiencies from reconstructed candidates at LHCb

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

The paper describes a framework for estimating trigger efficiencies using a data-driven approach that relies on the properties of reconstructed candidates rather than simulations. It introduces the TriggerCalib software package as the first centralized implementation of this framework for use in LHCb physics analyses. Trigger efficiency estimates are crucial for correcting measurements in particle physics experiments because the trigger system decides which events are recorded. The work also details methods for calculating statistical and systematic uncertainties associated with these estimates. This matters because it enables more reliable and less simulation-dependent results in high-energy physics studies.

Core claim

The authors present a data-driven framework to estimate trigger efficiencies from the properties of reconstructed candidates and implement it in the TriggerCalib package, which can be employed seamlessly in physics analyses at LHCb while providing ways to estimate uncertainties.

What carries the argument

The data-driven estimation framework based on properties of reconstructed candidates, implemented centrally in the TriggerCalib package.

If this is right

  • Trigger efficiencies can be calculated directly from data samples in LHCb analyses.
  • The TriggerCalib package serves as a standard tool for these calculations across different analyses.
  • Both statistical and systematic uncertainties on the efficiencies are accounted for within the same framework.
  • Analyses can avoid potential biases from simulation-based efficiency estimates.

Where Pith is reading between the lines

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

  • This approach could lead to improved precision in measurements of rare decays or production cross sections by reducing systematic errors from efficiency calculations.
  • Similar frameworks might be adapted for trigger systems in other experiments like ATLAS or CMS.
  • Future extensions could incorporate machine learning techniques to refine the estimation from candidate properties.

Load-bearing premise

The properties of reconstructed candidates provide an unbiased representation of the trigger response without significant distortions from reconstruction or selection procedures.

What would settle it

Observing a large discrepancy between efficiencies calculated with this data-driven method and those verified using a fully independent technique, such as detailed simulation studies or dedicated control channels, would indicate the claim does not hold.

Figures

Figures reproduced from arXiv: 2505.15951 by Abhijit Mathad, Alessandro Scarabotto, James Andrew Gooding, Johannes Albrecht, Maxim Lysenko, Tomasz Skwarnicki.

Figure 1
Figure 1. Figure 1: Distributions of the B+ candidate pT for simu￾lated B+→ J/ψ (µ +µ −) K+ decays selected by TIS de￾cisions and without any decisions required. It is noted that the TIS sample contains 5.7× fewer events than the sample with no requirement imposed. ratios of these as per-event weights to apply to simu￾lated samples of the channel of interest. For example, the latter approach was used in the LHCb measure￾ment … view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of the J/ψK+ invariant mass dis￾tribution for a single pT bin, overlaid with the results of the likelihood fit. 5 10 15 20 25 Transverse momentum [GeV] 0 20000 40000 60000 80000 100000 C a n d i d a t e s [ 0.5 G e V ] LHCb Simulation Unweighted candidates Combinatorial sWeighted candidates Signal sWeighted candidates [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Distributions of candidate (above) invari [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Trigger efficiencies evaluated on the B+→ J/ψ (µ +µ −) K+ simulated sample as a function of the pT of the B+ meson candidate. The different background mitigation methods implemented in the TriggerCalib package are compared and return consis￾tent results. The TriggerCalib software package allows the cal￾culation of correction weights to the trigger response of simulated samples. The correction weights for e… view at source ↗
read the original abstract

Estimations of trigger efficiencies are essential to modern particle physics analyses. A data-driven method provides a framework in which to estimate these efficiencies from the properties of reconstructed candidates, described in this paper. This paper also presents the design, implementation and performance of a software package, TriggerCalib, which provides a first centralised implementation of these calculations and can be seamlessly employed in physics analyses. Additionally, the estimation of statistical and systematic uncertainties is discussed.

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.

Referee Report

0 major / 3 minor

Summary. The manuscript presents a data-driven framework for estimating trigger efficiencies at LHCb from the properties of reconstructed candidates. It describes the design, implementation, and performance of the TriggerCalib software package as a first centralized implementation of these calculations, along with methods for estimating statistical and systematic uncertainties to support integration into physics analyses.

Significance. If the framework and implementation perform as described, the work provides a practical, reusable tool that standardizes an established data-driven technique across LHCb analyses. The centralization of the code and explicit treatment of uncertainties represent a clear community benefit for reproducibility. The manuscript ships a concrete software package rather than an abstract prescription, which strengthens its utility.

minor comments (3)
  1. [§2.2] §2.2: the description of how the efficiency is extracted from the reconstructed-candidate sample would benefit from an explicit statement of the functional form used for the efficiency parametrization.
  2. [Figure 3] Figure 3: the legend and axis labels are too small for readability in print; consider increasing font size or splitting into two panels.
  3. [§4.3] §4.3: the discussion of systematic uncertainties references an external note without summarizing the dominant sources; a short table of the main contributions would improve clarity.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive review of our manuscript and for recommending minor revision. We appreciate the assessment that the TriggerCalib framework and software package provide a practical, reusable tool for standardizing data-driven trigger efficiency estimation at LHCb, along with explicit uncertainty treatment.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper presents a practical data-driven framework for estimating trigger efficiencies directly from properties of reconstructed candidates, along with a centralised software implementation (TriggerCalib) and associated uncertainty treatment. No mathematical derivation chain, fitted parameter renamed as prediction, or load-bearing self-citation is described that would reduce the central result to its own inputs by construction. The contribution is implementation-focused and relies on standard data-driven techniques applied to external reconstructed data, remaining self-contained without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on standard domain assumptions in experimental particle physics about the usability of reconstructed candidates for efficiency estimation; no free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption Properties of reconstructed candidates can be used to estimate trigger efficiencies in a data-driven manner.
    This is the core premise of the framework presented in the abstract.

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