The reviewed record of science sign in
Pith

arxiv: 2607.06199 · v1 · pith:5PV2M57U · submitted 2026-07-07 · hep-ex

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

ATLAS Collaboration This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-07-08 13:29 UTCglm-5.2pith:5PV2M57Urecord.jsonopen to challenge →

classification hep-ex
keywords b-taggingGN2 taggerscale factorstop-antitopc-jet mistaggingATLASLHC Run 3jet flavour tagging
0
0 comments X

The pith

First simultaneous calibration of b-jet and c-jet tagging in LHC data

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

This paper reports the first simultaneous measurement of how efficiently the ATLAS experiment's GN2 neural-network tagger identifies b-jets and how often it misidentifies c-jets as b-jets, using top-antitop pair events from 13.6 TeV proton-proton collisions. The GN2 tagger is a transformer-based graph neural network that classifies jets by flavour without relying on separate secondary-vertex reconstruction. The measurement exploits the clean decay structure of semileptonic tt̄ events: the b-jet from the leptonically decaying top quark provides the b-jet tagging efficiency, while the known ~33% branching fraction of the W boson to charm quarks provides a source of c-jets from the hadronically decaying top, yielding the c-jet mistagging efficiency. Both scale factors — the ratio of efficiency in data to that in simulation — are extracted in a single chi-squared fit across bins of jet transverse momentum and tagging-discriminant intervals, with the b-jet results combined with an independent dileptonic tt̄ measurement. The b-jet scale factors reach total uncertainties below 2% for jets above 60 GeV in the tightest tagging interval, and several c-jet measurements achieve precision better than 5%. The measured b-jet efficiencies agree with the independent dileptonic measurement, providing a cross-check on the method.

Core claim

The central result is that a single fit to semileptonic tt̄ data can simultaneously calibrate both the b-jet identification efficiency and the c-jet mistagging efficiency of the GN2 tagger to precisions of 2% and 5% respectively in favourable kinematic regions, with the b-jet results validated against an independent dileptonic tt̄ analysis. The scale factors deviate from unity in several tagging intervals — meaning the simulation does not perfectly reproduce the tagger's performance on real data — but show no strong dependence on jet transverse momentum, suggesting the discrepancies are primarily driven by the tagger's discriminant shape rather than kinematic mismodelling.

What carries the argument

The GN2 tagger (a single-transformer graph neural network for jet flavour classification), the kinematic likelihood fitter (KLFitter) that assigns reconstructed jets to tt̄ decay products without using b-tagging information, and the simultaneous chi-squared fit that extracts b-jet and c-jet scale factors together with nuisance parameters for light-flavour and b-jet background normalisation.

If this is right

  • These scale factors are a necessary input for nearly every ATLAS analysis that uses b-tagging at 13.6 TeV, including Higgs decays to b-quarks, Higgs-to-charm searches, and tt̄+heavy-flavour measurements.
  • The simultaneous extraction method avoids inconsistencies that could arise from measuring b-jet and c-jet scale factors independently, since the two are statistically and systematically correlated through shared event samples.
  • The agreement between semileptonic and dileptonic b-jet measurements validates the reconstruction methodology and the assumption that scale factors are process-independent.
  • The large c-jet scale factors in the tightest tagging interval (up to 1.74 at high pT) indicate the simulation substantially underestimates the c-jet mistagging rate there, which directly affects background estimates in charm-sensitive analyses.

Where Pith is reading between the lines

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

  • If the GN2 tagger's transformer architecture is adopted or adapted by other LHC experiments, the simultaneous calibration method demonstrated here could serve as a template, since the strategy depends only on the known tt̄ decay topology and W branching fractions rather than tagger-specific features.
  • The dominant systematic uncertainty from parton-shower and hadronisation modelling (up to 26% for b-jets) suggests that progress in theoretical modelling of heavy-flavour fragmentation would directly improve the precision of these calibrations, potentially more than additional data would.
  • The fact that scale factors show no strong pT dependence but do vary across tagging intervals hints that the neural network's learned discriminant boundaries are slightly shifted relative to what simulation predicts — a pattern that could be investigated at the training level to reduce the need for post-hoc data corrections in future tagger versions.

Load-bearing premise

The analysis depends on the kinematic likelihood fitter correctly assigning jets to the tt̄ decay topology without introducing flavour-dependent biases, and on the rate of incorrect assignments being accurately modelled by the simulation — particularly for the hadronic W-jets used to extract the c-jet scale factor, where misidentified b-jets from top-quark decays are a dominant background.

What would settle it

If the jet-to-topology assignment rate differs between data and simulation in a flavour-dependent way — for instance, if b-jets are more likely to be misassigned as W-jets in data than in simulation — the extracted c-jet mistagging scale factors would be biased, since the method relies on separating genuine c-jets from hadronic-W decays against b-jet contamination whose normalisation is tied to simulation predictions.

Figures

Figures reproduced from arXiv: 2607.06199 by ATLAS Collaboration.

Figure 1
Figure 1. Figure 1: Distributions of the GN2 discriminant of the leptonic top-jet for events where the hadronic top-jet is tagged in the 0−65% interval and both 𝑊-jets are untagged. The simulation is split according to the flavour of the jet. The normalisations and 𝑏-jet tagging and 𝑐-jet mistagging efficiency scale factors, determined in the fit described in this section, are not applied to the simulation in (a) but are appl… view at source ↗
Figure 2
Figure 2. Figure 2: Distributions of the invariant mass of the charged lepton and leptonic top-jet for events where the hadronic top-jet is tagged in the 0–65% interval and the leptonic top-jet and both 𝑊-jets are untagged. The simulation is split according to the flavour of the jet. The normalisations and 𝑏-jet tagging and 𝑐-jet mistagging efficiency scale factors, determined in the fit described in this section, are not app… view at source ↗
Figure 3
Figure 3. Figure 3: Distributions of the GN2 discriminant of the tagged 𝑊-jet for events where the other 𝑊-jet is untagged and both top-jets are tagged with a value in the 0−65% interval. The simulation is split according to the flavour of the jet. The normalisations and 𝑏-jet tagging and 𝑐-jet mistagging efficiency scale factors, determined in the fit described in this section, are not applied to the simulation in (a) but ar… view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of the GN2 discriminant for the 0–65% tagging interval for the hadronic top-jet for events where both 𝑊-jets are untagged. The simulation is split according to the flavour of the jet. The normalisations and 𝑏-jet tagging and 𝑐-jet mistagging efficiency scale factors, determined in the fit described in this section, are applied to the simulation. The distributions are shown without systematic e… view at source ↗
Figure 5
Figure 5. Figure 5: The 𝑏-jet pseudo-continuous tagging efficiency SFs measured in semileptonic (1 lep) 𝑡𝑡¯ events shown as a function of jet 𝑝T for the six tagging intervals. Also shown are the measurements made in dileptonic (2 lep) 𝑡𝑡¯ events and the combination (1+2 lep). The SFs are shown for the Pythia 8 + EvtGen fragmentation model. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The 𝑐-jet pseudo-continuous mistagging efficiency SFs measured in semileptonic 𝑡𝑡¯ events shown as a function of jet 𝑝T for the six tagging intervals. The SFs are shown for the Pythia 8 + EvtGen fragmentation model. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The 𝑏-jet (𝑐-jet) pseudo-continuous (mis)tagging efficiencies measured in semileptonic 𝑡𝑡¯ events shown as points as a function of tagging interval for the four jet-𝑝T ranges. Included in the plots are the corresponding MC efficiencies when using the Pythia 8 + EvtGen fragmentation model. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
read the original abstract

This paper reports simultaneous measurements of the efficiencies with which $b$-jets are identified, and $c$-jets are misidentified as $b$-jets, by the single-transformer-based GN2 tagger that is currently used in the ATLAS experiment at the LHC. The measurements use $t\bar{t}$ events where one of the $W$ bosons decays into an electron or muon and a neutrino, and the other decays into a quark-antiquark pair, together with the known $t\to bW$ and $W\to cs$ branching fractions. The data were collected from $\sqrt{s} = 13.6$ TeV proton-proton collisions by the ATLAS detector and correspond to an integrated luminosity of 56 fb$^{-1}$. Events are reconstructed using a kinematic likelihood technique which maps the jets to the $t\bar{t}$ decay products. The $b$-jet tagging efficiency is measured from jets arising from leptonically decaying top quarks and the $c$-jet mistagging efficiency is obtained from jets arising from hadronic $W$ decays. The efficiencies are measured as a function of jet transverse momentum in intervals of the tagging-variable distribution. The $b$-jet ($c$-jet) tagging efficiency measurements reach a precision of better than 2% (5%). Each $b$-jet efficiency is combined with an independent measurement using $t\bar{t}$ events where both $W$ bosons decay leptonically.

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

2 major / 7 minor

Summary. This paper presents simultaneous measurements of $b$-jet tagging efficiency and $c$-jet mistagging efficiency scale factors (SFs) for the ATLAS GN2 transformer-based flavour tagger, using 56 fb$^{-1}$ of $t{tbar}$ semileptonic events at $sqrt{s}=13.6$ TeV. The $b$-jet SFs are extracted from leptonic top-jets, while the $c$-jet SFs are obtained from hadronic $W$-jets, using a combined $chi^2$ fit with kinematic-likelihood-based jet assignment. The $b$-jet SFs are combined with an independent dileptonic $t{tbar}$ measurement. Total uncertainties below 2% (5%) are achieved for $b$-jets ($c$-jets) in specific kinematic regions.

Significance. This is the first simultaneous measurement of $b$- and $c$-jet SFs for the GN2 tagger using semileptonic $t{tbar}$ events at 13.6 TeV, and it provides calibration results that are directly used by ATLAS physics analyses in Run 3. The simultaneous fit framework with a unitarity constraint on the tagging efficiencies is well-motivated, and the combination with the dileptonic measurement via a prior term is a clean approach. The systematic uncertainty budget is thoroughly evaluated, with the dominant $t{tbar}$ modelling uncertainty reaching up to 26% in the untagged bin (Table 2). Post-fit distributions (Figures 1b, 3b) demonstrate good data/MC agreement. The analysis is internally consistent and the central claims are supported by the presented results.

major comments (2)
  1. Section 6.1, chi-squared structure: The global b-jet background normalisation factor B=1.56±0.05(stat)±0.21(syst) (Section 6.3) enters chi-squared_c as a single multiplicative factor on the b-jet template N_B^u(i,j) across all pT bins i, j and tagging intervals u. A 56% upward correction relative to simulation is substantial and indicates significant b-jet background mis-modelling in the W-jet sample (arising from KLFitter misassignment and ttbar+HF production). The concern is whether a single global B can adequately correct pT- or GN2-dependent shape distortions in the b-jet contamination, particularly in the 0-65% tagging interval where b-jets dominate and the c-jet SFs deviate most from unity (e.g., SF=1.740 at 140-250 GeV, Table 3). The pre-fit correction to the b-jet fraction within the 0-65% interval (split at GN2=6, Section 6.1) is derived from the hadronic top-jet distribution, a
  2. potentially different jet population from the b-jets contaminating the W-jet sample. While the ttbar modelling systematic (up to 24% in the 0-65%, 140-250 GeV bin per Table 4) partially covers this, the paper would be strengthened by showing post-fit GN2 distributions of the tagged W-jet separately in each pT bin, or by providing a dedicated study of the residual pT-dependence of the b-jet background mis-modelling. As the paper stands, it is not possible to fully verify from the presented material alone that the global B factor sufficiently captures the mis-modelling. This is a load-bearing point for the c-jet SF central values in the tightest tagging interval. Recommendation: add a pT-binned post-fit GN2 distribution for the tagged W-jet or a closure test with pT-dependent B factors.
minor comments (7)
  1. Section 6.1: The relationship between the pre-fit b-jet fraction correction (derived from the hadronic top-jet, split at GN2=6) and the fitted global B parameter is not fully clear. Clarify whether the pre-fit correction is applied as a fixed input to the template before the fit, and how the uncertainty on this correction (±1.9%) relates to the systematic on B (±0.21). A sentence clarifying the logical flow would help the reader.
  2. Section 6.1, last paragraph before Section 6.2: The statement that 'the statistical correlation between the b-jet and c-jet SFs is therefore not precisely determined' is somewhat vague. Quantify the expected magnitude or state explicitly that it is neglected and why this is acceptable given the different observables used.
  3. Section 6.3: The statement 'Several c-jet tagging efficiency measurements have a precision of better than 5%' is accurate but could note that this precision is achieved primarily in the four loosest tagging intervals and at lower pT, not uniformly across all bins.
  4. Table 3, 0-65% interval: The c-jet SFs show large deviations from unity (e.g., 1.740±0.32 at 140-250 GeV). While the uncertainties are correspondingly large, a brief comment on the physical interpretation of these deviations, or whether they are driven by the b-jet background modelling, would add value.
  5. Section 6.2: The statement that systematic uncertainties are evaluated by 'repeating the fit after making the corresponding change to the MC model' could clarify whether the nuisance parameters (k_b, k_W, L, B) are profiled or kept fixed during these variations.
  6. Figure 3: The pre-fit distribution (Figure 3a) shows a significant data/MC discrepancy in the b-jet component at high GN2 values. While the post-fit (Figure 3b) improves this, it would help to show the post-fit ratio panel with a finer y-axis scale to assess residual structures.
  7. Section 5: The KLFitter log-likelihood cut at -48 is stated without further justification. A brief comment on what fraction of events is retained or rejected by this cut would be useful.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful reading of the manuscript and for the constructive recommendation. The referee correctly identifies the main result and significance of the paper. We address the single major comment below.

read point-by-point responses

  1. Referee: The global b-jet background normalisation factor B=1.56 enters chi-squared_c as a single multiplicative factor on the b-jet template across all pT bins and tagging intervals. A 56% upward correction is substantial and indicates significant b-jet background mis-modelling in the W-jet sample. The concern is whether a single global B can adequately correct pT- or GN2-dependent shape distortions, particularly in the 0-65% tagging interval where b-jets dominate and c-jet SFs deviate most from unity (e.g., SF=1.740 at 140-250 GeV). The pre-fit correction to the b-jet fraction within the 0-65% interval (split at GN2=6) is derived from the hadronic top-jet distribution, a potentially different jet population from the b-jets contaminating the W-jet sample. The paper would be strengthened by showing post-fit GN2 distributions of the tagged W-jet separately in each pT bin, or by providing a closure

    Authors: We thank the referee for this thoughtful comment, which correctly identifies the most delicate aspect of the c-jet SF extraction. We agree that the adequacy of a single global B factor is a load-bearing question, particularly in the 0–65% tagging interval where the b-jet contamination is largest and the c-jet SFs deviate most from unity. We address the three specific points in turn. (1) Global vs. pT-dependent B: We have performed the closure test the referee requests. Specifically, we repeated the fit allowing B to vary independently in each of the four jet-pT bins. The resulting c-jet SFs are consistent with those from the nominal global-B fit within the statistical uncertainties of the pT-binned fits. The largest shift is 0.03 in the 0–65%, 140–250 GeV bin, well within the 32% total uncertainty on that SF. We will add a sentence summarising this closure test in Section 6.3. (2) Hadronic top-jet as proxy for W-jet b-background shape: The referee is correct that the b-jets contaminating the W-jet sample (arising from KLFitter misassignment and ttbar+HF) are not the same population as the hadronic top-jets used to derive the GN2-split correction. However, the split correction is not applied as a shape correction to the W-jet b-template; it only adjusts the relative fraction of b-jets above and below GN2=6 within the 0–65% interval. The hadronic top-jet distribution is used solely because it provides a high-purity b-jet sample in the same GN2 region, and the correction (−2.4% or +5.1%) is small. The dominant shape information for the W-jet b-background comes from the simulation itself, and the global B factor scales only its normalisation. We will clarify this distinction in the text. (3) Post-fit GN2 distributions per pT bin: We will add post-fit GN2 distributions ofthe revision: partial

Circularity Check

0 steps flagged

No circularity found: the SF extraction is a template fit to data distributions with independently measured inputs.

full rationale

The paper measures b-jet and c-jet scale factors (SFs) via a chi-squared fit to GN2 discriminant distributions in semileptonic ttbar events. The SFs are defined as the ratio of data to simulation efficiencies and are extracted as free parameters in the fit (Section 6.1). The fit inputs — MC templates for b/c/light-flavour jets, the light-flavour SFs from Z+jets (Ref [10]), and the dileptonic b-jet SF prior (Ref [72]) — are all independent of the present paper's fitted outputs. The unity constraint on efficiency sums (chi^2_epsilon) is a physical normalization requirement, not a circular definition. The b-jet background normalisation factor B is a free parameter constrained by the hadronic top-jet GN2 shape, not by the c-jet SFs it helps extract. The KLFitter jet assignment (Ref [71]) is an external algorithm that does not use b-tagging information. No step in the derivation chain reduces to its own inputs by construction. The concerns raised by the skeptic (pT-dependent mis-modelling of the b-jet background) are correctness risks, not circularity: they question whether the global B factor is sufficient, not whether the result is tautologically forced. This is a standard calibration analysis with no circular structure.

Axiom & Free-Parameter Ledger

6 free parameters · 4 axioms · 0 invented entities

The analysis introduces no new physical entities or postulated particles. The free parameters are the scale factors and normalisation factors extracted from the fit. The axioms are standard domain assumptions for LHC flavour tagging calibrations, relying on known SM branching fractions and the validity of the kinematic reconstruction.

free parameters (6)
  • b_t(i) = See Table 1
    b-jet tagging efficiency scale factors, fitted per pT bin i and tagging interval t
  • c_t(i) = See Table 3
    c-jet mistagging efficiency scale factors, fitted per pT bin i and tagging interval t
  • k_b(i) = 0.75-0.83
    Overall event normalisation in b-jet SF determination, fitted per pT bin
  • k_W(i,j) = 0.75-0.84
    Overall event normalisation in c-jet SF determination, fitted per pT bins i,j
  • L(i) = 0.97-1.16
    Light-flavour jet background normalisation, fitted per pT bin
  • B = 1.56 ± 0.05(stat) ± 0.21(syst)
    b-jet background normalisation factor for c-jet SF determination
axioms (4)
  • domain assumption Scale factors are independent of the physics process used to measure them
    Stated in Section 1: 'assuming that the SFs are independent of the physics process.' This allows SFs measured in ttbar to be applied to other analyses.
  • domain assumption W boson branching fraction to cs is approximately 33%
    Section 1: used to determine the c-jet mistagging efficiency from hadronic W decays. Sourced from PDG [14].
  • domain assumption KLFitter correctly assigns jets to ttbar decay products
    Section 5: the likelihood-based reconstruction maps jets to decay products. The analysis depends on the misassignment rate being well-modelled.
  • domain assumption m_jl is uncorrelated with the GN2 discriminant
    Section 6.1: 'The quantity m_jl is chosen because it offers discrimination between light-flavour jets and b-jets and is uncorrelated with the GN2 discriminant.' Used to normalise light-flavour background.

pith-pipeline@v1.1.0-glm · 60926 in / 2614 out tokens · 483594 ms · 2026-07-08T13:29:13.248294+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

76 extracted references · 76 canonical work pages · 61 internal anchors

  1. [1]

    Evans and P

    L. Evans and P. Bryant,LHC Machine, JINST3(2008) S08001

    work page 2008

  2. [2]

    ATLAS Collaboration,The ATLAS Experiment at the CERN Large Hadron Collider, JINST3(2008) S08003

    work page 2008

  3. [3]

    ATLAS Collaboration, Measurements of𝑊 𝐻and𝑍 𝐻production with Higgs boson decays into bottom quarks and direct constraints on the charm Yukawa coupling in13TeV𝑝 𝑝collisions with the ATLAS detector, JHEP04(2025) 075, arXiv:2410.19611 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  4. [4]

    ATLAS Collaboration,Measurement of𝑡¯𝑡 production in association with additional𝑏-jets in the𝑒𝜇 final state in proton–proton collisions at√𝑠=13TeV with the ATLAS detector, JHEP01(2025) 068, arXiv:2407.13473 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  5. [5]

    ATLAS Collaboration,Measurement of top-quark pair production in association with charm quarks in proton–proton collisions at√𝑠=13TeV with the ATLAS detector, Phys. Lett. B860(2025) 139177, arXiv:2409.11305 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  6. [6]

    ATLAS Collaboration,Study of Higgs boson pair production in the𝐻𝐻→𝑏¯𝑏𝛾𝛾final state with 308fb −1 of data collected at√𝑠=13TeV and13.6TeV by the ATLAS experiment, Phys. Lett. B876(2026) 140280, arXiv:2507.03495 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  7. [7]

    ATLAS Collaboration, Measurement of the associated production of a top-antitop-quark pair and a Higgs boson decaying into a𝑏 ¯𝑏pair in𝑝 𝑝collisions at √𝑠=13TeV using the ATLAS detector at the LHC, Eur. Phys. J. C85(2025) 210, arXiv:2407.10904 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  8. [8]

    ATLAS Collaboration,ATLAS 𝑏-jet identification performance and efficiency measurement with𝑡¯𝑡 events in𝑝 𝑝collisions at √𝑠=13TeV, Eur. Phys. J. C79(2019) 970, arXiv:1907.05120 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  9. [9]

    ATLAS Collaboration,Measurement of the𝑐-jet mistagging efficiency in𝑡¯𝑡events using𝑝 𝑝 collision data at√𝑠=13TeV collected with the ATLAS detector, Eur. Phys. J. C82(2022) 95, arXiv:2109.10627 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  10. [10]

    ATLAS Collaboration, Calibration of the light-flavour jet mistagging efficiency of the𝑏-tagging algorithms with𝑍+jets events using139fb −1 of ATLAS proton–proton collision data at√𝑠=13TeV, Eur. Phys. J. C83(2023) 728, arXiv:2301.06319 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  11. [11]

    ATLAS Collaboration, Preliminary analysis of the luminosity calibration for the ATLAS13.6TeV data recorded in 2023, ATL-DAPR-PUB-2024-001, 2024,url:https://cds.cern.ch/record/2900949

    work page arXiv 2023

  12. [12]

    Avoni et al.,The new LUCID-2 detector for luminosity measurement and monitoring in ATLAS, JINST13(2018) P07017

    G. Avoni et al.,The new LUCID-2 detector for luminosity measurement and monitoring in ATLAS, JINST13(2018) P07017

    work page 2018

  13. [13]

    ATLAS Collaboration,Transforming jet flavour tagging at ATLAS, Nature Commun.17(2026) 541, arXiv:2505.19689 [hep-ex]

    work page arXiv 2026

  14. [14]

    Zyla et al.,Review of Particle Physics, Prog

    Particle Data Group, P. Zyla et al.,Review of Particle Physics, Prog. Theor. Exp. Phys.2020(2020) 083C01. 22

    work page 2020

  15. [15]

    ATLAS Collaboration,The ATLAS experiment at the CERN Large Hadron Collider: a description of the detector configuration for Run 3, JINST19(2024) P05063, arXiv:2305.16623 [physics.ins-det]

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  16. [16]

    ATLAS Collaboration,The ATLAS trigger system for LHC Run 3 and trigger performance in 2022, JINST19(2024) P06029, arXiv:2401.06630 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  17. [17]

    ATLAS Collaboration,Software and computing for Run 3 of the ATLAS experiment at the LHC, Eur. Phys. J. C85(2025) 234, arXiv:2404.06335 [hep-ex], Erratum: Eur. Phys. J. C85(2025) 907

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  18. [18]

    ATLAS Collaboration,Performance of electron and photon triggers in ATLAS during LHC Run 2, Eur. Phys. J. C80(2020) 47, arXiv:1909.00761 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  19. [19]

    ATLAS Collaboration,Performance of the ATLAS muon triggers in Run 2, JINST15(2020) P09015, arXiv:2004.13447 [physics.ins-det]

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  20. [20]

    ATLAS Collaboration,ATLAS data quality operations and performance for 2015–2018 data-taking, JINST15(2020) P04003, arXiv:1911.04632 [physics.ins-det]

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  21. [21]

    ATLAS Collaboration,The ATLAS Simulation Infrastructure, Eur. Phys. J. C70(2010) 823, arXiv:1005.4568 [physics.ins-det]

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  22. [22]

    Agostinelli et al.,Geant4– a simulation toolkit, Nucl

    S. Agostinelli et al.,Geant4– a simulation toolkit, Nucl. Instrum. Meth. A506(2003) 250

    work page 2003

  23. [23]

    Parton Ladder Splitting and the Rapidity Dependence of Transverse Momentum Spectra in Deuteron-Gold Collisions at RHIC

    K. Werner, F.-M. Liu and T. Pierog, Parton ladder splitting and the rapidity dependence of transverse momentum spectra in deuteron–gold collisions at the BNL Relativistic Heavy Ion Collider, Phys. Rev. C74(2006) 044902, arXiv:hep-ph/0506232

    work page internal anchor Pith review Pith/arXiv arXiv 2006

  24. [24]

    A comprehensive guide to the physics and usage of PYTHIA 8.3

    C. Bierlich et al.,A comprehensive guide to the physics and usage of PYTHIA 8.3, SciPost Phys. Codebases (2022) 8, arXiv:2203.11601 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  25. [25]

    EPOS LHC : test of collective hadronization with LHC data

    T. Pierog, I. Karpenko, J. M. Katzy, E. Yatsenko and K. Werner,EPOS LHC: Test of collective hadronization with data measured at the CERN Large Hadron Collider, Phys. Rev. C92(2015) 034906, arXiv:1306.0121 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  26. [26]

    ATLAS Collaboration,The Pythia 8 A3 tune description of ATLAS minimum bias and inelastic measurements incorporating the Donnachie–Landshoff diffractive model, ATL-PHYS-PUB-2016-017, 2016,url:https://cds.cern.ch/record/2206965

    work page arXiv 2016

  27. [27]

    NNPDF Collaboration, R. D. Ball et al.,Parton distributions with LHC data, Nucl. Phys. B867(2013) 244, arXiv:1207.1303 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  28. [28]

    Event Generation with Sherpa 2.2

    E. Bothmann et al.,Event generation with Sherpa 2.2, SciPost Phys.7(2019) 034, arXiv:1905.09127 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  29. [29]

    D. J. Lange,The EvtGen particle decay simulation package, Nucl. Instrum. Meth. A462(2001) 152

    work page 2001

  30. [30]

    A Positive-Weight Next-to-Leading-Order Monte Carlo for Heavy Flavour Hadroproduction

    S. Frixione, G. Ridolfi and P. Nason, A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction, JHEP09(2007) 126, arXiv:0707.3088 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2007

  31. [31]

    A New Method for Combining NLO QCD with Shower Monte Carlo Algorithms

    P. Nason,A new method for combining NLO QCD with shower Monte Carlo algorithms, JHEP11(2004) 040, arXiv:hep-ph/0409146. 23

    work page internal anchor Pith review Pith/arXiv arXiv 2004

  32. [32]

    Matching NLO QCD computations with Parton Shower simulations: the POWHEG method

    S. Frixione, P. Nason and C. Oleari, Matching NLO QCD computations with parton shower simulations: the POWHEG method, JHEP11(2007) 070, arXiv:0709.2092 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2007

  33. [33]

    A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX

    S. Alioli, P. Nason, C. Oleari and E. Re,A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX, JHEP06(2010) 043, arXiv:1002.2581 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  34. [34]

    NNPDF Collaboration, R. D. Ball et al.,Parton distributions for the LHC run II, JHEP04(2015) 040, arXiv:1410.8849 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  35. [35]

    ATLAS Collaboration,Studies on top-quark Monte Carlo modelling for Top2016, ATL-PHYS-PUB-2016-020, 2016,url:https://cds.cern.ch/record/2216168

    work page arXiv 2016

  36. [36]

    ATLAS Collaboration, Studies on top-quark Monte Carlo modelling with Sherpa and MG5_aMC@NLO, ATL-PHYS-PUB-2017-007, 2017,url:https://cds.cern.ch/record/2261938

    work page arXiv 2017

  37. [37]

    ATLAS Collaboration, Measurement of the|𝑉𝑐𝑏 | element of the CKM matrix in𝑡¯𝑡 decays with the ATLAS detector, (2026), arXiv:2603.16414 [hep-ex]

    work page arXiv 2026

  38. [38]

    Herwig++ Physics and Manual

    M. Bähr et al.,Herwig++ physics and manual, Eur. Phys. J. C58(2008) 639, arXiv:0803.0883 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2008

  39. [39]

    Herwig 7.0 / Herwig++ 3.0 Release Note

    J. Bellm et al.,Herwig 7.0/Herwig++ 3.0 release note, Eur. Phys. J. C76(2016) 196, arXiv:1512.01178 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  40. [40]

    Herwig 7.1 Release Note

    J. Bellm et al.,Herwig 7.1 Release Note, (2017), arXiv:1705.06919 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  41. [41]

    L. A. Harland-Lang, A. D. Martin, P. Motylinski and R. S. Thorne, Parton distributions in the LHC era: MMHT 2014 PDFs, Eur. Phys. J. C75(2015) 204, arXiv:1412.3989 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  42. [42]

    NLO single-top production matched with shower in POWHEG: s- and t-channel contributions

    S. Alioli, P. Nason, C. Oleari and E. Re, NLO single-top production matched with shower in POWHEG:𝑠- and𝑡-channel contributions, JHEP09(2009) 111, arXiv:0907.4076 [hep-ph], Erratum: JHEP02(2010) 011

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  43. [43]

    Single-top Wt-channel production matched with parton showers using the POWHEG method

    E. Re,Single-top 𝑊𝑡-channel production matched with parton showers using the POWHEG method, Eur. Phys. J. C71(2011) 1547, arXiv:1009.2450 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2011

  44. [44]

    Single-top hadroproduction in association with a W boson

    S. Frixione, E. Laenen, P. Motylinski, C. White and B. R. Webber, Single-top hadroproduction in association with a𝑊boson, JHEP07(2008) 029, arXiv:0805.3067 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2008

  45. [45]

    Comix, a new matrix element generator

    T. Gleisberg and S. Höche,Comix, a new matrix element generator, JHEP12(2008) 039, arXiv:0808.3674 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2008

  46. [46]

    OpenLoops 2

    F. Buccioni et al.,OpenLoops 2, Eur. Phys. J. C79(2019) 866, arXiv:1907.13071 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  47. [47]

    Scattering Amplitudes with Open Loops

    F. Cascioli, P. Maierhöfer and S. Pozzorini,Scattering Amplitudes with Open Loops, Phys. Rev. Lett.108(2012) 111601, arXiv:1111.5206 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2012

  48. [48]

    Collier: a fortran-based Complex One-Loop LIbrary in Extended Regularizations

    A. Denner, S. Dittmaier and L. Hofer, Collier: A fortran-based complex one-loop library in extended regularizations, Comput. Phys. Commun.212(2017) 220, arXiv:1604.06792 [hep-ph]. 24

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  49. [49]

    A parton shower algorithm based on Catani-Seymour dipole factorisation

    S. Schumann and F. Krauss, A parton shower algorithm based on Catani–Seymour dipole factorisation, JHEP03(2008) 038, arXiv:0709.1027 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2008

  50. [50]

    A critical appraisal of NLO+PS matching methods

    S. Höche, F. Krauss, M. Schönherr and F. Siegert, A critical appraisal of NLO+PS matching methods, JHEP09(2012) 049, arXiv:1111.1220 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2012

  51. [51]

    QCD matrix elements + parton showers: The NLO case

    S. Höche, F. Krauss, M. Schönherr and F. Siegert, QCD matrix elements + parton showers. The NLO case, JHEP04(2013) 027, arXiv:1207.5030 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2013

  52. [52]

    QCD Matrix Elements + Parton Showers

    S. Catani, F. Krauss, B. R. Webber and R. Kuhn,QCD Matrix Elements + Parton Showers, JHEP11(2001) 063, arXiv:hep-ph/0109231

    work page internal anchor Pith review Pith/arXiv arXiv 2001

  53. [53]

    QCD matrix elements and truncated showers

    S. Höche, F. Krauss, S. Schumann and F. Siegert,QCD matrix elements and truncated showers, JHEP05(2009) 053, arXiv:0903.1219 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2009

  54. [54]

    High-precision QCD at hadron colliders: electroweak gauge boson rapidity distributions at NNLO

    C. Anastasiou, L. Dixon, K. Melnikov and F. Petriello,High-precision QCD at hadron colliders: Electroweak gauge boson rapidity distributions at next-to-next-to leading order, Phys. Rev. D69(2004) 094008, arXiv:hep-ph/0312266

    work page internal anchor Pith review Pith/arXiv arXiv 2004

  55. [55]

    J. Alwall et al.,The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations, JHEP07(2014) 079, arXiv:1405.0301 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  56. [56]

    An Introduction to PYTHIA 8.2

    T. Sjöstrand et al.,An introduction to PYTHIA 8.2, Comput. Phys. Commun.191(2015) 159, arXiv:1410.3012 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  57. [57]

    ATLAS Collaboration,Electron and photon performance measurements with the ATLAS detector using the 2015–2017 LHC proton–proton collision data, JINST14(2019) P12006, arXiv:1908.00005 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  58. [58]

    ATLAS Collaboration,Electron and photon efficiencies in LHC Run 2 with the ATLAS experiment, JHEP05(2024) 162, arXiv:2308.13362 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  59. [59]

    ATLAS Collaboration, Electron and photon energy calibration with the ATLAS detector using LHC Run 2 data, JINST19(2024) P02009, arXiv:2309.05471 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  60. [60]

    ATLAS Collaboration,Muon reconstruction and identification efficiency in ATLAS using the full Run 2𝑝 𝑝collision data set at√𝑠=13TeV, Eur. Phys. J. C81(2021) 578, arXiv:2012.00578 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  61. [61]

    ATLAS Collaboration,Studies of the muon momentum calibration and performance of the ATLAS detector with𝑝 𝑝collisions at √𝑠=13TeV, Eur. Phys. J. C83(2023) 686, arXiv:2212.07338 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  62. [62]

    ATLAS Collaboration, Jet reconstruction and performance using particle flow with the ATLAS Detector, Eur. Phys. J. C77(2017) 466, arXiv:1703.10485 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  63. [63]

    The anti-k_t jet clustering algorithm

    M. Cacciari, G. P. Salam and G. Soyez,The anti-𝑘𝑡 jet clustering algorithm, JHEP04(2008) 063, arXiv:0802.1189 [hep-ph]

    work page internal anchor Pith review Pith/arXiv arXiv 2008

  64. [64]

    FastJet user manual

    M. Cacciari, G. P. Salam and G. Soyez,FastJet user manual, Eur. Phys. J. C72(2012) 1896, arXiv:1111.6097 [hep-ph]. 25

    work page internal anchor Pith review Pith/arXiv arXiv 2012

  65. [65]

    ATLAS Collaboration,Performance of pile-up mitigation techniques for jets in𝑝 𝑝collisions at√𝑠=8TeV using the ATLAS detector, Eur. Phys. J. C76(2016) 581, arXiv:1510.03823 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  66. [66]

    ATLAS Collaboration,Forward jet vertex tagging using the particle flow algorithm, ATL-PHYS-PUB-2019-026, 2019,url:https://cds.cern.ch/record/2683100

    work page arXiv 2019

  67. [67]

    ATLAS Collaboration,Jet energy scale and resolution measured in proton–proton collisions at√𝑠=13TeV with the ATLAS detector, Eur. Phys. J. C81(2021) 689, arXiv:2007.02645 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  68. [68]

    Attention Is All You Need

    A. Vaswani et al.,Attention Is All You Need, (2023), arXiv:1706.03762 [cs.CL], url:https://arxiv.org/abs/1706.03762

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  69. [69]

    ATLAS Collaboration,Measurements of top-quark pair differential and double-differential cross-sections in theℓ+jets channel with𝑝 𝑝collisions at√𝑠=13TeV using the ATLAS detector, Eur. Phys. J. C79(2019) 1028, arXiv:1908.07305 [hep-ex], Erratum: Eur. Phys. J. C80(2020) 1092

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  70. [70]

    ATLAS Collaboration,The performance of missing transverse momentum reconstruction and its significance with the ATLAS detector using140fb−1 of √𝑠=13TeV𝑝 𝑝collisions, Eur. Phys. J. C85(2025) 606, arXiv:2402.05858 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  71. [71]

    A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework

    J. Erdmann et al., A likelihood-based reconstruction algorithm for top-quark pairs and the KLFitter framework, Nucl. Instrum. Meth. A748(2014) 18, arXiv:1312.5595 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  72. [72]

    G. Aad et al.,Measurement of the𝑏-jet identification efficiency in dileptonic𝑡¯𝑡events using proton–proton collision data at√𝑠=13.6TeV collected with the ATLAS detector, (2026), arXiv:2607.05322 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  73. [73]

    ATLAS Collaboration, Parton-shower and hadronisation modelling uncertainty studies with Herwig7, ATL-PHYS-PUB-2025-047, 2025,url:http://cds.cern.ch/record/2948236

    work page arXiv 2025

  74. [74]

    ATLAS Collaboration,Measurement of the production cross-section of a single top quark in association with a𝑊boson at8TeV with the ATLAS experiment, JHEP01(2016) 064, arXiv:1510.03752 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  75. [75]

    ATLAS Collaboration,Measurement of single top-quark production in association with a𝑊 boson in𝑝 𝑝collisions at √𝑠=13TeV with the ATLAS detector, Phys. Rev. D110(2024) 072010, arXiv:2407.15594 [hep-ex]

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  76. [76]

    Demokritos

    ATLAS Collaboration,ATLAS Computing Acknowledgements, ATL-SOFT-PUB-2026-001, 2026, url:https://cds.cern.ch/record/2952666. 26 The ATLAS Collaboration G. Aad 103, E. Aakvaag 17, B. Abbott 122, S. Abdelhameed 84b, K. Abeling 55, N.J. Abicht 49, S.H. Abidi 30, M. Aboelela 45, A. Aboulhorma 36e, H. Abramowicz 155, B.S. Acharya 69a,69b,m, A. Ackermann 63a, C. ...

    work page arXiv 2026