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arxiv: 2607.05322 · v1 · pith:WAT7JYOE · submitted 2026-07-06 · hep-ex

Measurement of the b-jet identification efficiency in dileptonic tbar{t} events using proton-proton collision data at sqrt{s}=13.6 TeV collected with the ATLAS detector

The ATLAS Collaboration This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-07-07 17:07 UTCglm-5.2pith:WAT7JYOErecord.jsonopen to challenge →

classification hep-ex
keywords b-jet taggingGN2transformerATLAStop quark pairsscale factorsflavour tagging calibrationLHC Run 3
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The pith

Transformer b-tagger calibrated to 1% precision in ATLAS data

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

The paper measures how efficiently the GN2 algorithm — a transformer-based neural network for identifying jets containing b-hadrons — tags b-jets in real proton-proton collision data collected by the ATLAS detector at the LHC. Using a sample of 56 inverse femtobarns of data from 2022–2023 at a centre-of-mass energy of 13.6 TeV, the analysis selects events where top-quark pairs decay to two leptons plus two jets, providing a clean source of b-jets. A maximum-likelihood fit simultaneously extracts the b-jet tagging efficiency and the jet flavour composition across six bins of the GN2 discriminant and four ranges of jet transverse momentum (20–400 GeV). The resulting simulation-to-data scale factors range from 0.9 to 1.3, with total uncertainties as low as approximately 1% for jets with transverse momentum above 60 GeV in the tightest tagging bin. The paper also reports that GN2 achieves up to a factor of two higher light-flavour jet rejection and three times higher charm-flavour jet rejection compared to its predecessor DL1d at the same b-jet efficiency. These scale factors are delivered as correction weights for all ATLAS physics analyses using Run 3 data.

Core claim

The central result is a set of simulation-to-data scale factors for the GN2 b-jet tagger, derived from a simultaneous likelihood extraction of b-jet efficiency and flavour composition in dileptonic top-quark-pair events. The GN2 algorithm, which uses a transformer architecture to classify jet flavour from charged-particle track properties, is validated against real collision data with corrections ranging from 0.9 to 1.3 and precisions reaching 1% in the tightest efficiency bin at moderate jet transverse momentum. This confirms that the simulation adequately models the tagger performance in data and provides the calibration needed for all downstream ATLAS measurements that rely on b-jet_ident

What carries the argument

GN2 discriminant

If this is right

  • All ATLAS Run 3 physics analyses that rely on b-jet identification — including Higgs-to-bottom-quark measurements, Higgs-to-charm searches, and top-quark precision measurements — can now apply these scale factors to correct simulation predictions for data-vs-simulation differences in tagging efficiency.
  • The factor-of-two to factor-of-three rejection improvement of GN2 over DL1d translates directly into better signal-to-background separation, potentially increasing the sensitivity of searches for new physics and rare Standard Model processes.
  • The pseudo-continuous binning structure (PCBT) allows analyses to choose their own working point along the efficiency-rejection trade-off curve, with calibration available at each point rather than only at a fixed threshold.
  • The simultaneous fit of flavour composition correction factors (ranging 0.7–1.3) provides an independent cross-check of the Monte Carlo modelling of jet flavour content in top-quark-pair events.

Load-bearing premise

The likelihood model fixes the non-b-jet mis-tagging rates from Monte Carlo simulation (corrected by an external calibration) rather than treating them as free parameters, so any residual bias in the mis-tagging calibration or in the simulated flavour composition would be absorbed into the extracted b-jet efficiency — an effect most pronounced at low jet transverse momentum where the non-b-jet fraction is largest.

What would settle it

If the external light-jet mis-tagging calibration or the Monte Carlo modelling of non-bb flavour composition is biased, the extracted b-jet efficiency scale factors would absorb that bias, most visibly in the loosest tagging bin at low jet transverse momentum where total uncertainties already reach 25%.

read the original abstract

This paper presents the performance of the identification of jets containing $b$-hadrons ($b$-jets) for the GN2 algorithm, a transformer-based model for jet flavour tagging, using data collected by the ATLAS detector at the LHC. The analysis uses proton-proton collision data recorded in 2022 and 2023 at a centre-of-mass energy of $\sqrt{s} = 13.6$ TeV, corresponding to an integrated luminosity of 56 fb$^{-1}$. The $b$-jet identification efficiency and jet flavour composition are extracted simultaneously from a sample enriched in top-quark pair events ($t\bar{t}$). This efficiency is measured as a function of the jet transverse momentum in the range of 20-400 GeV and across six intervals of cumulative efficiency as derived in simulated $t\bar{t}$ events: [100%, 90%], [90%, 85%], [85%, 77%], [77%, 70%], [70%, 65%], and [65%, 0%]. The GN2 algorithm demonstrates significant performance improvements over its predecessor DL1d, achieving up to a factor of two (three) higher rejection of light-flavour (charm-flavour) jets at the same $b$-jet efficiency. The measured efficiencies in data are compared with simulation to derive correction factors ranging from 0.9 to 1.3. The total uncertainty is around 1% for jets with transverse momentum larger than 60 GeV in the [65%, 0%] interval of cumulative efficiency.

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 / 8 minor

Summary. This paper reports the measurement of the GN2 b-jet tagging efficiency in dileptonic tt̄ events using 56 fb⁻¹ of pp collision data at √s = 13.6 TeV collected by the ATLAS detector. The method employs a maximum-likelihood fit that simultaneously extracts 20 b-jet efficiency parameters (constrained to unitarity, yielding 5 free per pT bin) and 40 jet flavour-composition correction factors across a signal region and three control regions defined by jet–lepton invariant mass. The light-jet mis-tagging rates are fixed from simulation corrected by externally-derived scale factors from a dedicated Z+jets calibration. Scale factors ranging from 0.9 to 1.3 are measured across six PCBT bins and jet pT from 20–400 GeV, with total uncertainties as low as ~1% for jets with pT > 60 GeV in the [65%, 0%] bin. The GN2 algorithm is shown to achieve up to a factor of two (three) higher light-flavour (charm-flavour) jet rejection compared to its predecessor DL1d at the same b-jet efficiency in simulation.

Significance. This is the first in-situ calibration of the GN2 transformer-based b-tagger using Run 3 data, and it directly enables the ATLAS Run 3 physics programme that relies on b-jet identification. The measurement achieves percent-level precision in the high-purity, high-pT regime, which is the operationally most important region for Higgs and BSM searches. The simultaneous extraction of efficiency and flavour composition in a well-constructed likelihood with 390 bins, unitarity constraints, and pseudo-data bias testing is a methodological strength. The systematic uncertainty budget is comprehensive, with explicit evaluation of the mis-tagging rate impact. The paper follows the established methodology of Ref. [21] (the Run 2 DL1d calibration), providing continuity and allowing cross-checks with the previous generation of taggers.

minor comments (8)
  1. Section 5, equation for ν_SR: The P_l terms (light-jet mis-tagging rates) are fixed from MC with externally-derived scale factors rather than floated as free parameters. While the impact is evaluated as a systematic (Section 6, Table 3: 2% in the most sensitive bin, <0.5% elsewhere), the paper could strengthen its case by briefly discussing whether the c-factors (which adjust normalisation per flavour category but not the shape of P_l across PCBT bins) could partially absorb a P_l shape bias, or explicitly stating why this is not a concern given the CR constraints.
  2. Section 6: The statement that V+jets and diboson cross-section uncertainties 'were found to be negligible in previous measurement [21] and are therefore not included' would benefit from a brief quantitative justification specific to this analysis (e.g., the non-tt̄ fraction in the SR and the expected sensitivity), given the different centre-of-mass energy, integrated luminosity, and tagger relative to Ref. [21].
  3. Section 7, Table 2: Only data statistical uncertainties are quoted for the flavour-composition correction factors. A brief statement on the size of systematic uncertainties on these parameters, or an explicit note that they are dominated by the quoted statistical component, would be informative.
  4. Section 6: The mis-tagging scale factors are stated to vary between 1.3 and 1.5 for most PCBT bins (significantly deviating from unity). Given that these corrections are load-bearing for the P_l terms in the SR prediction, a brief comment on the origin of this sizeable deviation (e.g., whether it is expected from the GN2 training or from known MC deficiencies) would help the reader assess the robustness of the external calibration.
  5. Section 5: No goodness-of-fit metric (e.g., χ² or p-value) is reported for the likelihood fit. Including one, or stating that the post-fit data/MC agreement (shown in Figures 1, 2, 4) is the primary validation, would be useful.
  6. Section 7 / Conclusion: The paper states that 'the uncertainties and correlation scheme of uncertainty sources across jet pT and PCBT bins' are provided to ATLAS analyses. A brief note on how readers can access this information (e.g., reference to an auxiliary material or ATLAS internal note) would improve reproducibility for non-ATLAS readers.
  7. Figure 3: The axis labels and legend in this figure appear to be rendered with placeholder characters (boxes/symbols). This should be corrected for the final version.
  8. Abstract and Section 1: The performance improvement of GN2 over DL1d (factor of 2/3 rejection) is a simulation-level result. While this is clear from context, explicitly noting 'in simulation' in the abstract would avoid potential misinterpretation that this is a data-measured improvement.

Simulated Author's Rebuttal

0 responses · 0 unresolved

The referee recommends minor revision and raises no major comments. The report is positive and accurately summarizes the manuscript's content, methodology, and results. We thank the referee for the careful reading and constructive assessment.

Circularity Check

0 steps flagged

No circularity: the b-jet efficiency is extracted from data via a likelihood fit with independent inputs

full rationale

The paper measures the GN2 b-jet tagging efficiency in data using a maximum likelihood fit (Section 5, Eq. 1). The free parameters are P_b (20, with unitarity constraint) and flavour-composition correction factors c_bb, c_bl, c_lb, c_ll (40). The light-jet mis-tagging rates P_l are fixed from MC corrected by an external dedicated calibration (Ref. [69], a Z+jets analysis by ATLAS), not from this paper's own fit. The PCBT bin boundaries are discriminant thresholds defined from simulation, not fitted quantities. The method follows Ref. [21] (a prior ATLAS publication) as a documented procedure, not as a load-bearing uniqueness claim. The scale factors SF = P_b^Data / P_b^MC are computed as ratios of the fitted data efficiency to the MC prediction, which are independent quantities. No step in the derivation chain reduces to its own inputs by construction. The external mis-tagging calibration (Ref. [69]) is a separate analysis with different data samples and methodology, providing independent input rather than a self-citation chain. The fit is validated with pseudo-data bias tests. No circularity is present.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

No new particles, forces, or entities are postulated. This is a measurement paper.

free parameters (4)
  • P_b(O_k|T_m) = 20 parameters (5 per pT bin × 4 pT bins), determined by likelihood fit to data
    b-jet tagging efficiencies in each PCBT bin and pT interval, the central measured quantities
  • c^{m,n}_{bb,bl,lb,ll} = 40 parameters (10 per pT bin pair × 4 flavour categories), determined by likelihood fit
    Jet flavour composition correction factors, fitted simultaneously with P_b to account for data/MC differences in flavour composition
  • f_c = 0.2
    Charm fraction coefficient in the GN2 discriminant D_GN2, optimised on tt̄ MC (Section 1)
  • f_tau = 0.01
    Tau fraction coefficient in the GN2 discriminant D_GN2, optimised on tt̄ MC (Section 1)
axioms (4)
  • domain assumption The light-jet mis-tagging rate P_l from MC, corrected by external scale factors from Ref. [69], accurately represents the true non-b-jet tagging rate in data
    Section 5, ν_SR equation: P_l terms are fixed from MC, not free parameters in the fit. If biased, the extracted P_b absorbs the error.
  • domain assumption The tt̄ MC modelling (Powheg+Pythia8 with A14 tune) adequately describes the kinematic distributions used to define SR and CRs
    Section 3 and Section 4: the SR/CR definitions based on m_{j,ℓ} < 175 GeV rely on the MC-predicted kinematic endpoint. Alternative generators (Herwig7) are used only for systematic uncertainty, not as a cross-check of the central result.
  • domain assumption The likelihood model factorises the joint probability of both jets' PCBT responses as P_b(O_k|T_m) × P_b(O_p|T_n), i.e., the tagging decisions of the two jets are independent given their pT
    Section 5, ν_SR equation: the bb term uses P_b(O_k|T_m)·P_b(O_p|T_n), assuming no correlation between the two jets' tagging responses beyond pT dependence.
  • domain assumption The non-prompt electron rate uncertainty, derived from same-charge events in three coarse pT bins, adequately covers the true mis-modelling
    Section 6: the correction factors (up to 5.5 for 2022 data at low pT) are applied as reweighting factors with only 3 pT bins, which may not capture the full pT dependence.

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discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Simultaneous efficiency measurements of $b$- and $c$-jets in $t\bar{t}$ events from $\sqrt{s}=13.6$ TeV $pp$ collision data collected with the ATLAS detector

    hep-ex 2026-07 accept novelty 6.0

    Simultaneous measurements of b-jet tagging and c-jet mistagging efficiency scale factors for the ATLAS GN2 tagger achieve precisions below 2% and 5% respectively using 56 fb⁻¹ of 13.6 TeV ttbar data.

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