Pith. sign in

REVIEW 2 major objections 4 minor 55 references

Energy scale and resolution for anti-$k_t$ jets with radius parameters $R=0.2$ and 0.6 measured in proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector

T0 review · 2 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper calibrates anti-$k_t$ jets with radius parameters $R=0.2$ and $R=0.6$ to roughly the same precision as standard $R=0.4$ jets, using a new in situ method that matches them directly to $R=0.4$ reference jets in only two topologies.

desk verdict Solid first Run 2 JES/JER for R=0.2 and R=0.6 jets with a new direct-matching calibration; the headline resolution number is inconsistent between abstract and conclusions and must be fixed. read the letter →

arxiv 2412.15783 v2 pith:DND4BJ3Z submitted 2024-12-20 hep-ex

ATLAS Collaboration This is my paper · ORCID
classification hep-ex
keywords jetenergyscaleresolutionanti-k_tjetsalternativeradiusdirectmatchingmethodinsitucalibrationclose-byhadronicactivityLHC13TeVproton-protoncollisions
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 calibrates the energy scale and resolution of anti-$k_t$ jets (the standard infrared-safe jet clustering algorithm used at the LHC) with radius parameters $R=0.2$ and $R=0.6$, using 37 fb$^{-1}$ of 13 TeV proton--proton collisions. Its central proposal is a new in situ 'direct matching method' that calibrates these alternative-radius jets against the already well-calibrated $R=0.4$ jets in only $Z$+jets and dijet events, instead of repeating the full multi-topology calibration for each radius. The paper claims that this is enough to bring the total jet energy scale uncertainty for both radii to roughly the same size as for the reference $R=0.4$ jets, about 1% for central jets with $100 \lesssim p_T \lesssim 800$ GeV. The measured relative energy resolution for central $R=0.2$ jets falls from $(35\pm6)\%$ at $p_T=20$ GeV to $(6\pm0.5)\%$ at $p_T=300$ GeV. These numbers matter because $R=0.2$ jets are the building blocks of boosted-object substructure measurements, while $R=0.6$ jets serve analyses that need a wider angular catchment.

What carries the argument

The central object is the direct matching in situ calibration. Alternative-radius jets are matched within $\Delta R<0.2$ to fully calibrated anti-$k_t$ $R=0.4$ reference jets reconstructed from EM-scale topoclusters, and the correction $C(p_T^{\mathrm{ref}},\eta^{\mathrm{AR}})=\langle R_{\mathrm{insitu}}\rangle^{\mathrm{MC}}/\langle R_{\mathrm{insitu}}\rangle^{\mathrm{Data}}$ is built from the average $p_T$ ratio in $Z$+jets and dijet events. This transfers the full $R=0.4$ calibration, including its propagated systematic uncertainties, onto the new radii without re-running the complete four-topology in situ program. For the resolution, the supporting machinery is the standard three-term parameterisation $\sigma(p_T)/p_T = N/p_T \oplus S/\sqrt{p_T} \oplus C$, with the noise term $N$ fixed from random cones in zero-bias data and the stochastic and constant terms $S$ and $C$ fitted from the dijet balance asymmetry. The close-by hadronic activity study uses the inverse charged fraction $r_{\mathrm{trk}}=p_T^{\mathrm{probe}}/p_T^{\mathrm{trk}}$ as a calibration-independent probe of energy scale changes when other jets approach within $\Delta R\sim0.4$.

What would settle it

A decisive check is to derive the same in situ correction in a third topology, such as $\gamma$+jets, and compare the calibrated $R=0.2$ and $R=0.6$ jet energy scales with the $Z$+jets/dijet result: the paper's own closure tests show agreement within 0.5% to 1%, so a disagreement larger than the combined uncertainties would show that two topologies do not cover the full phase space. A second check is to re-derive the correction using 2017--2018 data and verify whether the claimed Run 2 applicability holds under higher pile-up conditions.

Watch

Extended reading notes

Core claim

The paper argues, on its own terms, that the energy scale of anti-$k_t$ jets with $R=0.2$ and $R=0.6$ can be established at the same precision as the standard $R=0.4$ jets. The load-bearing result is the direct matching method: each alternative-radius jet is matched to a fully calibrated $R=0.4$ jet, and a correction factor $C(p_T^{\mathrm{ref}},\eta^{AR})=\langle R_{\mathrm{insitu}}\rangle^{\mathrm{MC}}/\langle R_{\mathrm{insitu}}\rangle^{\mathrm{Data}}$ is derived from the $p_T$ ratio in $Z$+jets and dijet data and simulation. After statistical combination and smoothing in $(p_T,\eta_{\mathrm{det}})$, the total jet energy scale uncertainty for central jets is about 1% in the $100\lesssim p_T\lesssim800$ GeV range, with the $R=0.2$ budget dominated by propagated $R=0.4$ reference uncertainties and the low-$p_T$ $R=0.6$ budget by pile-up; the conclusion is that the uncertainty is of similar magnitude to that of reference jets. The resolution is determined by fixing the noise term from random-cone measurements and fitting the dijet direct-balance asymmetry; for central $R=0.2$ jets it decreases from $(35\pm6)\%$ at $p_T=20$ GeV to $(6\pm0.5)\%$ at $p_T=300$ GeV. The paper further claims that close-by hadronic activity shifts the $R=0.2$ scale in a way that is well modelled by Monte Carlo simulation, so no additional correction is needed when small jets are reclustered into large jets.

Load-bearing premise

The load-bearing premise is that the already-calibrated $R=0.4$ reference jets are unbiased and that the data-to-simulation response differences measured in $Z$+jets and dijet events represent all environments where $R=0.2$ and $R=0.6$ jets are used; if either assumption fails, the alternative-radius jet energy scale inherits the same hidden bias.

Editorial extensions

If this is right

  • Measurements using $R=0.2$ and $R=0.6$ jets would inherit roughly the same jet energy scale systematic precision as standard $R=0.4$ jets, about 1% for central jets with $100\lesssim p_T\lesssim800$ GeV.
  • The direct matching method, requiring only $Z$+jets and dijet topologies, provides a lightweight route for calibrating any alternative jet radius in future datasets or at other experiments.
  • The published $R=0.2$ resolution figures (35% at 20 GeV decreasing to 6% at 300 GeV for central jets) set the uncertainty budget for track-assisted reclustering and boosted-object analyses built from small-radius jets.
  • The close-by hadronic activity results imply that no additional jet energy scale correction is needed when $R=0.2$ jets are reclustered into large-radius jets, and that the quoted uncertainties cover boosted topologies up to $p_T\sim2.4$ TeV.

Reading between the lines

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

  • If the transfer works as cleanly as claimed, the same matching idea should calibrate other radii, such as $R=1.0$ or very small jets for high-pile-up running, and could be checked immediately against the dedicated large-radius calibration already published by the same experiment.
  • A structural limit is that this method's uncertainty can never be smaller than the reference $R=0.4$ calibration; an independent cross-check using single-particle-response calibrations, which the paper names as a future validation route, would expose any radius-dependent bias inherited from the reference.
  • The close-by activity study relies on charged-particle tracks, so a complementary test using calorimeter-only or neutral-sensitive observables would show whether the Monte Carlo modelling also holds for the neutral component of close-by radiation.
  • Since the calibration uses 2015--2016 data but is asserted to cover the whole Run 2, a natural extension is to check $Z$+jet balance or jet $p_T$ spectra in the higher-pile-up 2017--2018 data against these calibrations.
Share X Bluesky LinkedIn Reddit HN

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. The paper presents measurements of the jet energy scale (JES) and jet energy resolution (JER) for anti-k_t jets with radius parameters R=0.2 and R=0.6, using 37 fb^-1 of 13 TeV pp collisions recorded by ATLAS in 2015-2016. A new 'direct matching' in situ method calibrates AR jets against fully calibrated R=0.4 jets in Z+jet and dijet topologies. The paper reports JES uncertainties near 1% for 100-800 GeV central jets and JER values from (35±6)% at pT=20 GeV to about 6% at pT=300 GeV for central R=0.2 jets, with a dedicated study of close-by hadronic activity using 80.4 fb^-1 of 2015-2017 data. The central claim is that the direct matching method yields AR-jet JES uncertainties of similar magnitude to those of the reference R=0.4 jets.

Significance. If correct, the results are significant: they provide the first LHC calibration of anti-k_t R=0.2 jets, which are needed for substructure studies of boosted bosons and top quarks, and they generalize the in situ calibration approach by using R=0.4 jets as reference objects. The paper's strengths include a detailed decomposition of 69 JES and 101/102 JER uncertainty components, explicit closure tests with agreement typically within 0.5%, MC generator cross-checks with Sherpa and Herwig, consistency checks using chi-square metrics, and a close-by-activity study that validates the calibration in dense environments. The reliance on the R=0.4 reference jets is not circular, because those jets were calibrated independently in earlier ATLAS publications and the associated uncertainties are propagated; the main residual risk is the implicit assumption that the measured data/MC response difference is representative across the full phase space, which is mitigated by the MC modelling uncertainty and the demonstrated consistency of the two topologies. However, the paper contains internal inconsistencies in the concluding section that must be fixed before the results can be cited reliably.

major comments (2)
  1. [Section 8 (Conclusions) vs. Abstract and Figure 20] The conclusion states that the relative energy resolution ranges from (35±6)% at pT=20 GeV to (4±0.5)% at pT=300 GeV for central R=0.2 jets, while the Abstract states (6±0.5)% at pT=300 GeV. Figure 20(a) and the surrounding text in Section 6.2 are consistent with the Abstract value of about 6%, not 4%. This is a load-bearing numerical inconsistency in the headline result: most readers will quote the value from the Conclusions, and a 50% relative error in the quoted resolution is material. The Conclusions must be corrected to match the Abstract and Figure 20, and the paper should be checked for any other occurrences of this number.
  2. [Section 8 (Conclusions), JER method description] The Conclusions state that the full JER is 'determined using the direct balance method, where the resolutions are extracted by comparing asymmetries in energies of AR jets measured in data to those found in truth jet simulations, in the Z+jets and dijet topologies.' This is inconsistent with Section 6.2, where the direct balance method is defined using only dijet events; Z+jets events are used for the in situ JES calibration in Section 5.2, not for the JER measurement. The sentence should be corrected to refer only to the dijet topology, or to properly describe the separate roles of the two topologies.
minor comments (4)
  1. [Section 5.3.1 and Figures 16/23] The text in Sections 5.3.1 and 6.3.1 refers to a 'reducible set of 45 pT-dependent in situ uncertainty components,' while Section 5.3 states the total JES has 69 components; the relationship between the total count and the reducible set of 45 should be made explicit to avoid confusion.
  2. [Section 7] The close-by-activity study uses 80.4 fb^-1 of 2015-2017 data, whereas the main JES/JER calibration uses 37 fb^-1 of 2015-2016 data; this is stated but could be emphasized more prominently because the two luminosity figures appear in close succession and readers may otherwise mistake the close-by study as part of the main calibration sample.
  3. [Section 6.1] In the random cones description, the text says the noise term is estimated as one-half of the central 68% confidence interval divided by sqrt(2); the derivation of the factor sqrt(2) from the pair difference in Eq. (4) is implicit and could be stated more explicitly for clarity.
  4. [Section 4] The sentence 'While this approach creates a dependency of the AR jets reconstructed from topoclusters at the LCW scale on R=0.4 jets reconstructed from topoclusters at the EM scale' is grammatically awkward; rewording would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the AR jet calibration is a measurement against an independently calibrated R=0.4 reference, not a derivation that reduces to its own inputs.

full rationale

The paper's central quantities, the JES and JER for R=0.2 and R=0.6 jets, are obtained by measuring AR jets against R=0.4 reference jets whose calibration was established in a separate ATLAS publication (Ref. [4]). The direct matching correction C(pT_ref, eta_AR) = <R_in situ>_MC/<R_in situ>_Data is extracted from data/MC ratios in Z+jet and dijet topologies and is applied to data as a residual correction; it is not derived from the same quantity it is used to correct. The MC-based calibration stages (pile-up, absolute JES/eta, GSC) are derived from truth-jet matching in simulation and are validated by closure tests. The JER result combines an independent noise-term measurement from random cones with a direct balance measurement in dijet events; the low-pT values are constrained by the explicitly stated fixed noise term, but this is a transparent combination of complementary measurements rather than a circular reduction. No fitted parameter is renamed as a prediction, and no load-bearing uniqueness theorem or preferred ansatz is imported from the authors' prior work. The only notable internal issue is a numerical inconsistency: the abstract reports (6 ± 0.5)% for the central R=0.2 JER at pT=300 GeV while the conclusions report (4 ± 0.5)% at the same point; Figure 20 and the surrounding text support the abstract value. This is a correctness/self-consistency concern, not circularity, and does not affect the circularity score.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities. Free parameters are standard calibration coefficients (alpha, beta) and JER fit parameters (S, C), all with stated uncertainties. The main external input is the R=0.4 jet calibration, which is a prior measurement rather than an axiom, but is treated as trusted in this analysis.

free parameters (5)
  • alpha (residual pile-up dependence on NPV) = fitted per |eta_det| bin, typical values not shown
    Eq. 1: pT correction depends on alpha*(NPV-1), fitted to MC to remove residual pile-up dependence.
  • beta (residual pile-up dependence on mu) = fitted per |eta_det| bin
    Eq. 1: pT correction depends on beta*mu, fitted to MC to remove residual pile-up dependence.
  • Noise term N in JER parameterisation = measured via random cones, not a fit parameter
    Eq. 3: N is fixed to central value from random cones, but it is a measured constant with associated uncertainty; we include it as an input parameter to the JER fit.
  • Stochastic term S in JER parameterisation = fitted to data, values not quoted
    Eq. 3: S is a free parameter in the chi-square fit to the measured JER points.
  • Constant term C in JER parameterisation = fitted to data, values not quoted
    Eq. 3: C is a free parameter in the chi-square fit to the measured JER points.
assumptions (3)
  • domain assumption The R=0.4 jet calibration from Ref. [4] is accurate and applicable as a reference.
    Section 5.2 uses fully calibrated R=0.4 jets as reference; any bias in that calibration propagates to AJ jets.
  • domain assumption The ATLAS detector simulation (Geant4) accurately reproduces the detector response.
    Section 3 describes simulation; all MC-based calibrations and uncertainty estimates rely on this assumption.
  • standard math The anti-k_t jet algorithm and topocluster reconstruction are correctly implemented and appropriate.
    Section 4 relies on standard jet reconstruction tools; no new algorithmic claim.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Energy scale and resolution for anti-$k_t$ jets with radius parameters $R=0.2$ and 0.6 measured in proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector." pith.science (2026). https://pith.science/paper/DND4BJ3Z

@misc{pith2026241215783,
  author       = {Pith},
  title        = {Pith review of: Energy scale and resolution for anti-$k_t$ jets with radius parameters $R=0.2$ and 0.6 measured in proton-proton collisions at $\sqrts = 13$ TeV with the ATLAS detector},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DND4BJ3Z}},
  note         = {Machine review of arXiv:2412.15783}
}
abstract

Jets with different radius parameters $R$ are an important tool to probe quantum chromodynamics processes at different angular scales. Jets with small $R=0.2$ are instrumental in measurements of the substructure of large-$R$ jets resulting from collimated hadronic decays of energetic $W$, $Z$, and Higgs bosons, top quarks, and of potential new resonances. Measurements are presented of the energy scale and resolution of jets with radius parameters $R=0.2$ and 0.6 obtained with the ATLAS detector using $37 \text{fb}^{-1}$ of proton--proton collisions from the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}=13$ TeV, with their associated uncertainties. A new in situ method to measure jet energy scale differences between data and Monte Carlo simulations is presented. The systematic uncertainties in the jet energy scale for central jets ($|\eta| < 1.2$) typically vary from 1% to about 5% as a function of $|\eta|$ at very low transverse momentum, $p_{\text{T}}$, of around 20 GeV for both $R=0.2$ and 0.6 jets. The relative energy resolution ranges from $(35 \pm 6)$% at $p_{\text{T}} = 20$ GeV to $(6 \pm 0.5)$% at $p_{\text{T}} = 300$ GeV for central $R=0.2$ jets, and is found to be slightly worse for $R=0.6$ jets. Finally, the effect of close-by hadronic activity on the jet energy scale is investigated and is found to be well modelled by the ATLAS Monte Carlo simulations.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

55 extracted references · 9 canonical work pages

  1. [1]

    Evans and P

    L. Evans and P. Bryant,LHC Machine, Journal of Instrumentation3(2008) S08001

    work page 2008

  2. [2]

    Cacciari, G

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

    arXiv 2008

  3. [3]

    Cacciari, G

    M. Cacciari, G. P. Salam and G. Soyez,FastJet user manual, Eur. Phys. J. C72 (2012) 1896, arXiv: 1111.6097 [hep-ph]. 41

    arXiv 2012

  4. [4]

    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]

    arXiv 2021

  5. [5]

    ATLAS Collaboration, Jet radius dependence of dijet momentum balance and suppression in Pb+Pb collisions at5.02TeV with the ATLAS detector, Phys. Rev. C110 (2024) 054912, arXiv: 2407.18796 [nucl-ex]

    arXiv 2024

  6. [6]

    ATLAS Collaboration, Luminosity determination in𝑝𝑝 collisions at√𝑠= 13TeV using the ATLAS detector at the LHC, Eur. Phys. J. C83(2023) 982, arXiv:2212.09379 [hep-ex]

    arXiv 2023

  7. [7]

    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

    2018

  8. [8]

    ATLAS Collaboration, Search for dark matter produced in association with a dark Higgs boson decaying into𝑊+𝑊− in the one-lepton final state at√𝑠= 13TeV using139fb−1 of𝑝𝑝 collisions recorded with the ATLAS detector, JHEP07(2023) 116, arXiv:2211.07175 [hep-ex]

    arXiv 2023

Show all 55 references
  1. [9]

    ATLAS Collaboration,Search for boosted low-mass resonances decaying into hadrons produced in association with a photon in𝑝𝑝 collisions at√𝑠= 13TeV with the ATLAS detector, (2024), arXiv: 2408.00049 [hep-ex]

    2024 arXiv

  2. [10]

    ATLAS Collaboration, Jet energy measurement with the ATLAS detector in proton–proton collisions at√𝑠= 7TeV, Eur. Phys. J. C73(2013) 2304, arXiv:1112.6426 [hep-ex]

    2013 arXiv

  3. [11]

    ATLAS Collaboration, Single hadron response measurement and calorimeter jet energy scale uncertainty with the ATLAS detector at the LHC, Eur. Phys. J. C73(2013) 2305, arXiv: 1203.1302 [hep-ex]

    2013 arXiv

  4. [12]

    ATLAS Collaboration,Jet energy resolution in proton–proton collisions at√𝑠= 7TeVrecorded in 2010 with the ATLAS detector, Eur. Phys. J. C73(2013) 2306, arXiv:1210.6210 [hep-ex]

    2013 arXiv

  5. [13]

    ATLAS Collaboration, Jet energy measurement and its systematic uncertainty in proton–proton collisions at√𝑠= 7TeV with the ATLAS detector, Eur. Phys. J. C75(2015) 17, arXiv: 1406.0076 [hep-ex]

    2015 arXiv

  6. [14]

    ATLAS Collaboration, Determination of jet calibration and energy resolution in proton–proton collisions at√𝑠= 8TeV using the ATLAS detector, Eur. Phys. J. C80 (2020) 1104, arXiv: 1910.04482 [hep-ex]

    2020 arXiv

  7. [15]

    ATLAS Collaboration, Jet energy scale measurements and their systematic uncertainties in proton–proton collisions at√𝑠= 13TeV with the ATLAS detector, Phys. Rev. D96(2017) 072002, arXiv: 1703.09665 [hep-ex]

    2017 arXiv

  8. [16]

    ATLAS Collaboration, New techniques for jet calibration with the ATLAS detector, Eur. Phys. J. C83(2023) 761, arXiv:2303.17312 [hep-ex]

    2023 arXiv

  9. [17]

    ATLAS Collaboration, A precise measurement of the jet energy scale derived from single-particle measurements and in situ techniques in proton–proton collisions at√𝑠= 13TeV with the ATLAS detector, (2024), arXiv: 2407.15627 [hep-ex]

    2024 arXiv

  10. [18]

    ATLAS Collaboration, In situ calibration of large-radius jet energy and mass in13TeV proton–proton collisions with the ATLAS detector, Eur. Phys. J. C79(2019) 135, arXiv: 1807.09477 [hep-ex]. 42

    2019 arXiv

  11. [19]

    ATLAS Collaboration, Jet reclustering and close-by effects in ATLAS Run 2, ATLAS-CONF-2017-062, 2017,url: https://cds.cern.ch/record/2275649

    2017

  12. [20]

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

    2008

  13. [21]

    ATLAS Collaboration, ATLAS Insertable B-Layer Technical Design Report, ATLAS-TDR-19; CERN-LHCC-2010-013, 2010, url: https://cds.cern.ch/record/1291633, Addendum: ATLAS-TDR-19-ADD-1; CERN-LHCC-2012-009, 2012,url: https://cds.cern.ch/record/1451888

    2010

  14. [22]

    ATLAS Electromagnetic Liquid Argon Group, Construction, assembly and tests of the ATLAS electromagnetic end-cap calorimeters, JINST3 (2008) P06002

    2008

  15. [23]

    ATLAS Collaboration, ATLAS Calorimeter Performance: Technical Design Report, ATLAS-TDR-1; CERN-LHCC-96-040, 1996,url: https://cds.cern.ch/record/331059

    1996

  16. [24]

    ATLAS Collaboration, Performance of the ATLAS trigger system in 2015, Eur. Phys. J. C77(2017) 317, arXiv:1611.09661 [hep-ex]

    2017 arXiv

  17. [25]

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

    2020 arXiv

  18. [26]

    Sjöstrand, S

    T. Sjöstrand, S. Mrenna and P. Skands,A brief introduction to PYTHIA 8.1, Comput. Phys. Commun.178 (2008) 852, arXiv:0710.3820 [hep-ph]

    2008 arXiv

  19. [27]

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

    2013 arXiv

  20. [28]

    Andersson, G

    B. Andersson, G. Gustafson, G. Ingelman and T. Sjöstrand, Parton fragmentation and string dynamics, Phys. Rept.97(1983) 31

    1983

  21. [29]

    ATLAS Collaboration, ATLAS Pythia 8 tunes to7TeV data, ATL-PHYS-PUB-2014-021, 2014, url: https://cds.cern.ch/record/1966419

    2014

  22. [30]

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

    2001

  23. [31]

    Alioli, P

    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]

    2010 arXiv

  24. [32]

    Lai et al.,New parton distributions for collider physics, Phys

    H.-L. Lai et al.,New parton distributions for collider physics, Phys. Rev. D82(2010) 074024, arXiv: 1007.2241 [hep-ph]

    2010 arXiv

  25. [33]

    Pumplin et al., New Generation of Parton Distributions with Uncertainties from Global QCD Analysis, JHEP07(2002) 012, arXiv:hep-ph/0201195

    J. Pumplin et al., New Generation of Parton Distributions with Uncertainties from Global QCD Analysis, JHEP07(2002) 012, arXiv:hep-ph/0201195

    2002 arXiv

  26. [34]

    ATLAS Collaboration, Example ATLAS tunes ofPythia8, Pythia6 and Powhegto an observable sensitive to𝑍 boson transverse momentum, ATL-PHYS-PUB-2013-017, 2013, url: https://cds.cern.ch/record/1629317

    2013

  27. [35]

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

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

    2019 arXiv

  28. [36]

    Catani, F

    S. Catani, F. Krauss, B. R. Webber and R. Kuhn,QCD Matrix Elements + Parton Showers, JHEP11(2001) 063, arXiv:hep-ph/0109231. 43

    2001 arXiv

  29. [37]

    Bellm et al.,Herwig 7.0/Herwig++ 3.0 release note, Eur

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

    2016 arXiv

  30. [38]

    NNPDF Collaboration, Parton distributions for the LHC run II, JHEP04(2015) 040, arXiv: 1410.8849 [hep-ph]

    2015 arXiv

  31. [39]

    Sjöstrand et al.,An introduction to PYTHIA 8.2, Comput

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

    2015 arXiv

  32. [40]

    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

    2016

  33. [41]

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

    2010 arXiv

  34. [42]

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

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

    2003

  35. [43]

    ATLAS Collaboration, Software and computing for Run 3 of the ATLAS experiment at the LHC, (2024), arXiv:2404.06335 [hep-ex]

    2024 arXiv

  36. [44]

    ATLAS Collaboration, Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1, Eur. Phys. J. C77(2017) 490, arXiv:1603.02934 [hep-ex]

    2017 arXiv

  37. [45]

    ATLAS Collaboration,Vertex Reconstruction Performance of the ATLAS Detector at√𝑠= 13 TeV, ATL-PHYS-PUB-2015-026, 2015,url: https://cds.cern.ch/record/2037717

    2015

  38. [46]

    ATLAS Collaboration,A neural network clustering algorithm for the ATLAS silicon pixel detector, JINST9 (2014) P09009, arXiv:1406.7690 [hep-ex]

    2014 arXiv

  39. [47]

    Cacciari and G

    M. Cacciari and G. P. Salam,Pileup subtraction using jet areas, Phys. Lett. B659 (2008) 119, arXiv: 0707.1378 [hep-ph]

    2008 arXiv

  40. [48]

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

    2021 arXiv

  41. [49]

    S. D. Ellis and D. E. Soper,Successive combination jet algorithm for hadron collisions, Phys. Rev. D48 (1993) 3160, arXiv:hep-ph/9305266

    1993 arXiv

  42. [50]

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

    2016 arXiv

  43. [51]

    ATLAS Collaboration, Evaluating statistical uncertainties and correlations using the bootstrap method, ATL-PHYS-PUB-2021-011, 2021,url: https://cds.cern.ch/record/2759945

    2021

  44. [52]

    ATLAS Collaboration, Pile-up subtraction and suppression for jets in ATLAS, ATLAS-CONF-2013-083, 2013,url: https://cds.cern.ch/record/1570994

    2013

  45. [53]

    ATLAS Collaboration, Track assisted techniques for jet substructure, ATL-PHYS-PUB-2018-012, 2018, url: https://cds.cern.ch/record/2630864

    2018

  46. [54]

    ATLAS Collaboration,Search for Dark Matter Produced in Association with a Dark Higgs Boson Decaying into𝑊±𝑊∓ or𝑍𝑍 in Fully Hadronic Final States from√𝑠= 13TeV𝑝𝑝 Collisions Recorded with the ATLAS Detector, Phys. Rev. Lett.126 (2021) 121802, arXiv: 2010.06548 [hep-ex]. 44

    2021 arXiv

  47. [55]

    Demokritos

    ATLAS Collaboration, ATLAS Computing Acknowledgements, ATL-SOFT-PUB-2023-001, 2023, url: https://cds.cern.ch/record/2869272. 45 The ATLAS Collaboration G. Aad 105, E. Aakvaag 17, B. Abbott 124, S. Abdelhameed 120a, K. Abeling 56, N.J. Abicht 50, S.H. Abidi 30, M. Aboelela 46, ...

    2023

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.