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arxiv: 2604.09809 · v1 · submitted 2026-04-10 · ✦ hep-ex · physics.ins-det

Recognition: 1 theorem link

· Lean Theorem

Particle transformers for identifying Lorentz-boosted Higgs bosons decaying to a pair of W bosons

CMS Collaboration
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:12 UTC · model grok-4.3

classification ✦ hep-ex physics.ins-det
keywords particle transformerboosted Higgsjet taggingmachine learningCMSdibosonself-attentionLorentz boost
0
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The pith

PaRT tags boosted Higgs-to-WW jets at over 50 percent efficiency with 1 percent background while staying independent of jet mass.

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

The paper introduces a self-attention neural network called PaRT to identify jets from highly Lorentz-boosted Higgs bosons decaying into pairs of W bosons. It trains the model on many simulated jet topologies and reports strong separation between signal and background. The algorithm is then calibrated directly in proton-proton collision data. If correct, this tagging method raises the reach of both standard-model diboson measurements and searches for new resonances that decay to hadronic final states.

Core claim

PaRT, a particle transformer that uses self-attention to weigh the contributions of individual particles inside a jet, identifies boosted Higgs bosons decaying to WW with tagging efficiency above 50 percent at a background efficiency of 1 percent while remaining decorrelated from the jet mass; data-to-simulation efficiency scale factors measured in 138 fb^{-1} of 13 TeV collisions fall between 0.9 and 1.0.

What carries the argument

The PaRT classifier, a self-attention network that assigns importance weights to the particles reconstructed inside each jet to discriminate multipronged signal jets from background.

If this is right

  • Standard-model measurements of diboson production gain sensitivity through improved signal selection.
  • Searches for beyond-standard-model resonances decaying to hadronic dibosons can exploit the higher tagging efficiency.
  • The mass-decorrelated output allows the jet mass to be used as an independent variable in fits or limits.

Where Pith is reading between the lines

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

  • The same architecture could be retrained on other boosted resonances such as Z or top quarks with minimal changes to the input representation.
  • Because the model operates on particle lists rather than fixed images, it may generalize to jets with variable numbers of constituents more readily than convolutional approaches.
  • Combining PaRT scores with existing substructure variables could further reduce background in analyses that already use jet mass.

Load-bearing premise

Simulated events used for training capture the particle distributions and detector responses that actually occur in proton-proton collisions.

What would settle it

A tag-and-probe measurement of the signal efficiency in a data control sample, such as a sideband or a known resonance, that deviates significantly from the predicted value after all corrections.

Figures

Figures reproduced from arXiv: 2604.09809 by CMS Collaboration.

Figure 1
Figure 1. Figure 1: Diagram of the PART model architecture. The model processes two different sets of input features per jet, from PF candidates and SVs. These features are embedded using sepa￾rate MLPs into 128-dimensional representations before being concatenated and passed through eight PABs. Pairwise features are also calculated between each input element, which are em￾bedded using a single one-dimensional convolutional l… view at source ↗
Figure 2
Figure 2. Figure 2: Full suite of AK8 jet topologies considered for the P [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Evolution of the loss function values for the P [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of jet mass reconstruction (mreco) using the SD, PARTICLENET, and PART algorithms, for H → bb (upper left), H → WW (upper right), t → bqq (lower left), and QCD (lower right) jets with the SM values of mH and mt . An offline selection is applied to the AK8 jets of pT > 400 GeV and |η| < 2.4. Statistical uncertainties in the bin yields originating from the limited number of simulated events are re… view at source ↗
Figure 5
Figure 5. Figure 5: Receiver operating characteristic curves for H [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Receiver operating characteristic curves for Y [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Confusion matrix with each row indicating the fraction of jets per category classified [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Distributions of mSD for jets from QCD multijet events, in the pT ranges 200–400 GeV (upper), 400–600 GeV (middle) and 600–1000 GeV (lower), after no selections (“inclusive”) on the PART THWW score (left) and the DEEPAK8-MD score (right) as well as selections corre￾sponding to QCD jet selection efficiencies (ϵB ) of 5.0%, 1.0%, and 0.5%. The error bars repre￾sent the statistical uncertainties originating f… view at source ↗
Figure 9
Figure 9. Figure 9: The Jensen–Shannon distance (JSD) using base 2 between the [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Schematic of the LJP calibration method for H [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: We consider the THWW discriminant from Eq. (6), excluding Ptop from the denominator to retain top quark events in the high tagger score bins: T No top HWW = PHWW4q + PHWW3q PQCD + PHWW4q + PHWW3q . (8) The T No top HWW distributions from the 2017 data sets before and after LJP-reweighting of the events with matched top quark jets are shown in [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 11
Figure 11. Figure 11: Distributions of the PART T No top HWW (left) and DEEPAK8-MD (No top) (right) discrimi￾nants with and without the LJP corrections for t-matched jets for data and individual simulated processes in the upper panels, and data versus simulation ratios in the lower panels. The com￾bined uncertainties from LJP-based SFs per bin are shown in shaded gray, and the statistical uncertainty in the number of data even… view at source ↗
read the original abstract

A novel deep neural network classifier, a ``Particle transformer'' (PaRT), is introduced for the identification of highly Lorentz-boosted resonances reconstructed as single, multipronged jets in measurements and searches performed by the CMS Collaboration at the CERN LHC. Based on a self-attention mechanism that allows the model to weigh the importance of different particles, PaRT is trained on a wide variety of topologies, notably demonstrating strong performance for the first time on jets originating from boosted Higgs boson decays to W bosons. The PaRT algorithm achieves a tagging efficiency of more than 50\% for such jets at a background efficiency of 1%, while maintaining decorrelation from the jet mass. A calibration is performed in proton-proton collision data collected by CMS at a center-of-mass energy of 13 TeV, with a data set corresponding to a total luminosity of 138 fb$^{-1}$. Data-to-simulation selection efficiency scale factors are measured to be in the 0.9$-$1.0 range, with relative uncertainties between 7 and 23%. The tagging capability of PaRT enhances the sensitivity of standard model measurements and searches for beyond-the-standard-model resonances decaying to hadronic diboson systems.

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 introduces a Particle Transformer (PaRT) deep neural network for identifying Lorentz-boosted Higgs bosons decaying to WW pairs, reconstructed as single multipronged jets. It reports a tagging efficiency exceeding 50% at 1% background efficiency while maintaining decorrelation from jet mass, trained on diverse simulated topologies, and provides a data calibration in 138 fb^{-1} of 13 TeV CMS collision data yielding scale factors of 0.9-1.0 with relative uncertainties of 7-23%. The work aims to enhance sensitivity for SM diboson measurements and BSM resonance searches.

Significance. If the reported performance and calibration hold, this provides a new tool for tagging boosted H->WW jets that could improve sensitivity in hadronic diboson analyses. The transformer architecture with self-attention on particle constituents represents a timely application to a difficult topology, and the explicit data-driven calibration step with scale factors near unity is a positive feature that mitigates simulation-to-data discrepancies.

minor comments (3)
  1. The abstract states the efficiency claim without referencing the specific pT or mass range over which it applies; this should be clarified in the results section with supporting figures.
  2. The data calibration section reports scale factors in the 0.9-1.0 range; it would strengthen the paper to include a table or plot showing the scale factors as a function of jet pT or mass, along with the breakdown of uncertainty sources.
  3. The claim of 'strong performance for the first time' on boosted H->WW should be supported by a direct comparison to existing taggers (e.g., DeepAK8 or ParticleNet) in the same topology, including efficiency curves.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our manuscript on the Particle Transformer (PaRT) algorithm and for recommending minor revision. We appreciate the recognition of the reported tagging performance for boosted H->WW jets, the decorrelation from jet mass, and the data-driven calibration yielding scale factors near unity. No specific major comments were provided in the report, so we have reviewed the manuscript for minor improvements in clarity and presentation while maintaining the core results.

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper trains PaRT on simulated topologies, reports tagging efficiencies (>50% signal at 1% background with mass decorrelation) on held-out simulation, and derives data-to-simulation scale factors (0.9-1.0) from independent 13 TeV collision data (138 fb^{-1}). No equation or claim reduces by construction to a fitted parameter from the same inputs, no self-definitional loop exists, and no load-bearing self-citation or ansatz smuggling is invoked for the central performance metrics. The separation of training, evaluation, and calibration datasets keeps the reported results independent of the inputs used to generate them.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that simulation faithfully represents real detector response for the relevant jet topologies and that the self-attention model generalizes without hidden biases.

axioms (1)
  • domain assumption Simulated events accurately model the detector response, particle fragmentation, and underlying event for boosted diboson jets.
    Standard assumption in all LHC machine-learning taggers; directly affects transfer of efficiency from simulation to data.

pith-pipeline@v0.9.0 · 5506 in / 1288 out tokens · 57798 ms · 2026-05-10T16:12:04.085521+00:00 · methodology

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. Improved results on Higgs boson pair production in the 4b final state

    hep-ex 2026-04 unverdicted novelty 4.0

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Reference graph

Works this paper leans on

93 extracted references · 84 canonical work pages · cited by 1 Pith paper · 10 internal anchors

  1. [1]

    Phys.Lett

    CMS Collaboration, “Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC”,Phys. Lett. B716(2012) 30, References 23 doi:10.1016/j.physletb.2012.08.021,arXiv:1207.7235

    work page doi:10.1016/j.physletb.2012.08.021 2012

  2. [2]

    Observation of a new boson with mass near 125 GeV in pp collisions at √s=7 and 8 TeV

    CMS Collaboration, “Observation of a new boson with mass near 125 GeV in pp collisions at √s=7 and 8 TeV”,JHEP06(2013) 081, doi:10.1007/JHEP06(2013)081,arXiv:1303.4571

    work page doi:10.1007/jhep06(2013)081 2013

  3. [3]

    Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC

    ATLAS Collaboration, “Observation of a new particle in the search for the standard model Higgs boson with the ATLAS detector at the LHC”,Phys. Lett. B716(2012) 1, doi:10.1016/j.physletb.2012.08.020,arXiv:1207.7214

    work page internal anchor Pith review doi:10.1016/j.physletb.2012.08.020 2012

  4. [4]

    Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

    CMS Collaboration, “Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques”,JINST15(2020) P06005, doi:10.1088/1748-0221/15/06/P06005,arXiv:2004.08262

    work page doi:10.1088/1748-0221/15/06/p06005 2020

  5. [5]

    Performance of the mass-decorrelated DEEPDOUBLEX classifier for double-b and double-c large-radius jets with the CMS detector

    CMS Collaboration, “Performance of the mass-decorrelated DEEPDOUBLEX classifier for double-b and double-c large-radius jets with the CMS detector”, CMS Detector Performance Summary CMS-DP-2022-041, 2022

    2022

  6. [6]

    JEDI-net: a jet identification algorithm based on interaction networks

    E. A. Moreno et al., “JEDI-net: a jet identification algorithm based on interaction networks”,Eur. Phys. J. C80(2020) 58,doi:10.1140/epjc/s10052-020-7608-4, arXiv:1908.05318

    work page doi:10.1140/epjc/s10052-020-7608-4 2020

  7. [7]

    Interaction networks for the identification of boosted H→b b decays

    E. A. Moreno et al., “Interaction networks for the identification of boosted H→b b decays”,Phys. Rev. D102(2020) 012010,doi:10.1103/PhysRevD.102.012010, arXiv:1909.12285

    work page doi:10.1103/physrevd.102.012010 2020

  8. [8]

    Qu and L

    H. Qu and L. Gouskos, “PARTICLENET: Jet tagging via particle clouds”,Phys. Rev. D101 (2020) 056019,doi:10.1103/PhysRevD.101.056019,arXiv:1902.08570

    work page doi:10.1103/physrevd.101.056019 2020

  9. [9]

    Identification of highly Lorentz-boosted heavy particles using graph neural networks and new mass decorrelation techniques

    CMS Collaboration, “Identification of highly Lorentz-boosted heavy particles using graph neural networks and new mass decorrelation techniques”, CMS Detector Performance Summary CMS-DP-2020-002, 2020

    2020

  10. [10]

    Mass regression of highly-boosted jets using graph neural networks

    CMS Collaboration, “Mass regression of highly-boosted jets using graph neural networks”, CMS Detector Performance Summary CMS-DP-2021-017, 2021

    2021

  11. [11]

    Measurement of boosted Higgs bosons produced via vector boson fusion or gluon fusion in the H→b b decay mode using LHC proton-proton collision data at √s=13 TeV

    CMS Collaboration, “Measurement of boosted Higgs bosons produced via vector boson fusion or gluon fusion in the H→b b decay mode using LHC proton-proton collision data at √s=13 TeV”,JHEP12(2024) 035,doi:10.1007/JHEP12(2024)035, arXiv:2407.08012

    work page doi:10.1007/jhep12(2024)035 2024

  12. [12]

    Search for Higgs boson decay to a charm quark-antiquark pair in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Search for Higgs boson decay to a charm quark-antiquark pair in proton-proton collisions at √s=13 TeV”,Phys. Rev. Lett.131(2023) 061801, doi:10.1103/PhysRevLett.131.061801,arXiv:2205.05550

    work page doi:10.1103/physrevlett.131.061801 2023

  13. [13]

    Search for nonresonant pair production of highly energetic Higgs bosons decaying to bottom quarks

    CMS Collaboration, “Search for nonresonant pair production of highly energetic Higgs bosons decaying to bottom quarks”,Phys. Rev. Lett.131(2023) 041803, doi:10.1103/PhysRevLett.131.041803,arXiv:2205.06667

    work page doi:10.1103/physrevlett.131.041803 2023

  14. [14]

    Search for a massive scalar resonance decaying to a light scalar and a Higgs boson in the four b quarks final state with boosted topology

    CMS Collaboration, “Search for a massive scalar resonance decaying to a light scalar and a Higgs boson in the four b quarks final state with boosted topology”,Phys. Lett. B842 (2023) 137392,doi:10.1016/j.physletb.2022.137392,arXiv:2204.12413. 24

    work page doi:10.1016/j.physletb.2022.137392 2023

  15. [15]

    Search for resonant pair production of Higgs bosons in the b bb b final state using large-area jets in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Search for resonant pair production of Higgs bosons in the b bb b final state using large-area jets in proton-proton collisions at √s=13 TeV”,JHEP02 (2025) 040,doi:10.1007/JHEP02(2025)040,arXiv:2407.13872

    work page doi:10.1007/jhep02(2025)040 2025

  16. [16]

    Search for heavy resonances decaying to a pair of lorentz-boosted higgs bosons in final states with leptons and a bottom quark pair at √s=13 TeV

    CMS Collaboration, “Search for heavy resonances decaying to a pair of Lorentz-boosted Higgs bosons in final states with leptons and a bottom quark pair at √s=13 TeV”,JHEP 05(2022) 005,doi:10.1007/JHEP05(2022)005,arXiv:2112.03161

    work page doi:10.1007/jhep05(2022)005 2022

  17. [17]

    Search for resonances decaying to three W bosons in the hadronic final state in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Search for resonances decaying to three W bosons in the hadronic final state in proton-proton collisions at √s=13 TeV”,Phys. Rev. D106(2022) 012002, doi:10.1103/PhysRevD.106.012002,arXiv:2112.13090

    work page doi:10.1103/physrevd.106.012002 2022

  18. [18]

    Search for resonances decaying to three W bosons in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Search for resonances decaying to three W bosons in proton-proton collisions at √s=13 TeV”,Phys. Rev. Lett.129(2022) 021802, doi:10.1103/PhysRevLett.129.021802,arXiv:2201.08476

    work page doi:10.1103/physrevlett.129.021802 2022

  19. [19]

    Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning

    A. J. Larkoski, I. Moult, and B. Nachman, “Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning”,Phys. Rept.841(2020) 1, doi:10.1016/j.physrep.2019.11.001,arXiv:1709.04464

    work page doi:10.1016/j.physrep.2019.11.001 2020

  20. [20]

    Particle Transformer for Jet Tagging

    H. Qu, C. Li, and S. Qian, “Particle transformer for jet tagging”, inProc. 39th Int. Conf. on Machine Learning, volume 162, p. 18281. 2022.arXiv:2202.03772

    work page arXiv 2022

  21. [21]

    Search for a massive resonance decaying into a Higgs boson and a W or Z boson in hadronic final states in proton-proton collisions at √s=8 TeV

    CMS Collaboration, “Search for a massive resonance decaying into a Higgs boson and a W or Z boson in hadronic final states in proton-proton collisions at √s=8 TeV”,JHEP 02(2016) 145,doi:10.1007/JHEP02(2016)145,arXiv:1506.01443

    work page doi:10.1007/jhep02(2016)145 2016

  22. [22]

    Theory and phenomenology of two-Higgs-doublet models

    G. C. Branco et al., “Theory and phenomenology of two-Higgs-doublet models”,Phys. Rept.516(2012) 1,doi:10.1016/j.physrep.2012.02.002,arXiv:1106.0034

    work page doi:10.1016/j.physrep.2012.02.002 2012

  23. [23]

    Searching for signs of the second Higgs doublet

    N. Craig, J. Galloway, and S. Thomas, “Searching for signs of the second Higgs doublet”, 2013.arXiv:1305.2424

    work page arXiv 2013

  24. [24]

    About the bosonic decays of heavy Higgs states in the (N)MSSM

    F. Domingo and S. Paßehr, “About the bosonic decays of heavy Higgs states in the (N)MSSM”,Eur. Phys. J. C82(2022) 962, doi:10.1140/epjc/s10052-022-10919-4,arXiv:2207.05776

    work page doi:10.1140/epjc/s10052-022-10919-4 2022

  25. [25]

    LHC signals from cascade decays of warped vector resonances

    K. S. Agashe et al., “LHC signals from cascade decays of warped vector resonances”, JHEP05(2017) 078,doi:10.1007/JHEP05(2017)078,arXiv:1612.00047

    work page doi:10.1007/jhep05(2017)078 2017

  26. [26]

    Dedicated strategies for triboson signals from cascade decays of vector resonances

    K. Agashe et al., “Dedicated strategies for triboson signals from cascade decays of vector resonances”,Phys. Rev. D99(2019) 075016,doi:10.1103/PhysRevD.99.075016, arXiv:1711.09920

    work page doi:10.1103/physrevd.99.075016 2019

  27. [27]

    Model-independent probe of anomalous heavy neutral Higgs bosons at the LHC

    H.-Y. Ren, L.-H. Xia, and Y.-P . Kuang, “Model-independent probe of anomalous heavy neutral Higgs bosons at the LHC”,Phys. Rev. D90(2014) 115002, doi:10.1103/PhysRevD.90.115002,arXiv:1404.6367

    work page doi:10.1103/physrevd.90.115002 2014

  28. [28]

    Further investigation of the model-independent probe of heavy neutral Higgs bosons at LHC Run 2

    Y.-P . Kuang, H.-Y. Ren, and L.-H. Xia, “Further investigation of the model-independent probe of heavy neutral Higgs bosons at LHC Run 2”,Chin. Phys. C40(2016) 023101, doi:10.1088/1674-1137/40/2/023101,arXiv:1506.08007

    work page doi:10.1088/1674-1137/40/2/023101 2016

  29. [29]

    The Lund jet plane

    F. A. Dreyer, G. P . Salam, and G. Soyez, “The Lund jet plane”,JHEP12(2018) 064, doi:10.1007/JHEP12(2018)064,arXiv:1807.04758. References 25

    work page doi:10.1007/jhep12(2018)064 2018

  30. [30]

    A method for correcting the substructure of multiprong jets using the Lund jet plane

    CMS Collaboration, “A method for correcting the substructure of multiprong jets using the Lund jet plane”,JHEP11(2025) 038,doi:10.1007/JHEP11(2025)038, arXiv:2507.07775

    work page doi:10.1007/jhep11(2025)038 2025

  31. [31]

    Combination of searches for nonresonant Higgs boson pair production in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Combination of searches for nonresonant Higgs boson pair production in proton-proton collisions at √s=13 TeV”, 2025.arXiv:2510.07527. Submitted toJ. Phys. G

    work page arXiv 2025

  32. [32]

    The CMS experiment at the CERN LHC

    CMS Collaboration, “The CMS experiment at the CERN LHC”,JINST3(2008) S08004, doi:10.1088/1748-0221/3/08/S08004

    work page doi:10.1088/1748-0221/3/08/s08004 2008

  33. [33]

    Development of the CMS detector for the CERN LHC Run 3

    CMS Collaboration, “Development of the CMS detector for the CERN LHC Run 3”, JINST19(2024) P05064,doi:10.1088/1748-0221/19/05/P05064, arXiv:2309.05466

    work page doi:10.1088/1748-0221/19/05/p05064 2024

  34. [34]

    Performance of the CMS Level-1 trigger in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Performance of the CMS Level-1 trigger in proton-proton collisions at √s=13 TeV”,JINST15(2020) P10017, doi:10.1088/1748-0221/15/10/P10017,arXiv:2006.10165

    work page doi:10.1088/1748-0221/15/10/p10017 2020

  35. [35]

    The CMS trigger system

    CMS Collaboration, “The CMS trigger system”,JINST12(2017) P01020, doi:10.1088/1748-0221/12/01/P01020,arXiv:1609.02366

    work page doi:10.1088/1748-0221/12/01/p01020 2017

  36. [36]

    Performance of the CMS high-level trigger during LHC Run 2

    CMS Collaboration, “Performance of the CMS high-level trigger during LHC Run 2”, JINST19(2024) P11021,doi:10.1088/1748-0221/19/11/P11021, arXiv:2410.17038

    work page doi:10.1088/1748-0221/19/11/p11021 2024

  37. [37]

    Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC

    CMS Collaboration, “Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC”,JINST16(2021) P05014, doi:10.1088/1748-0221/16/05/P05014,arXiv:2012.06888

    work page doi:10.1088/1748-0221/16/05/p05014 2021

  38. [38]

    Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $\sqrt{s}=$ 13 TeV

    CMS Collaboration, “Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √s=13 TeV”,JINST13(2018) P06015, doi:10.1088/1748-0221/13/06/P06015,arXiv:1804.04528

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1748-0221/13/06/p06015 2018

  39. [39]

    Description and performance of track and primary-vertex reconstruction with the CMS tracker

    CMS Collaboration, “Description and performance of track and primary-vertex reconstruction with the CMS tracker”,JINST9(2014) P10009, doi:10.1088/1748-0221/9/10/P10009,arXiv:1405.6569

    work page doi:10.1088/1748-0221/9/10/p10009 2014

  40. [40]

    The CMS phase-1 pixel detector upgrade

    CMS Tracker Group, “The CMS phase-1 pixel detector upgrade”,JINST16(2021) P02027,doi:10.1088/1748-0221/16/02/P02027,arXiv:2012.14304

    work page doi:10.1088/1748-0221/16/02/p02027 2021

  41. [41]

    Track impact parameter resolution for the full pseudo rapidity coverage in the 2017 dataset with the CMS phase-1 pixel detector

    CMS Collaboration, “Track impact parameter resolution for the full pseudo rapidity coverage in the 2017 dataset with the CMS phase-1 pixel detector”, CMS Detector Performance Summary CMS-DP-2020-049, 2020

    2017

  42. [42]

    2017 tracking performance plots

    CMS Collaboration, “2017 tracking performance plots”, CMS Detector Performance Summary CMS-DP-2017-015, 2017

    2017

  43. [43]

    Particle-flow reconstruction and global event description with the CMS detector

    CMS Collaboration, “Particle-flow reconstruction and global event description with the CMS detector”,JINST12(2017) P10003,doi:10.1088/1748-0221/12/10/P10003, arXiv:1706.04965

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1088/1748-0221/12/10/p10003 2017

  44. [44]

    Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid

    CMS Collaboration, “Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid”, CMS Technical Proposal CERN-LHCC-2015-010, CMS-TDR-15-02, 2015. 26

    2015

  45. [45]

    Offline secondary vertex reconstruction in the CMS detector

    CMS Collaboration, “Offline secondary vertex reconstruction in the CMS detector”,PoS LHCP2024(2025) 236,doi:10.22323/1.478.0236

    work page doi:10.22323/1.478.0236 2025

  46. [46]

    Cacciari, G.P

    M. Cacciari, G. P . Salam, and G. Soyez, “The anti-kT jet clustering algorithm”,JHEP04 (2008) 063,doi:10.1088/1126-6708/2008/04/063,arXiv:0802.1189

    work page doi:10.1088/1126-6708/2008/04/063 2008

  47. [47]

    Cacciari, G.P

    M. Cacciari, G. P . Salam, and G. Soyez, “FastJet user manual”,Eur. Phys. J. C72(2012) 1896,doi:10.1140/epjc/s10052-012-1896-2,arXiv:1111.6097

    work page doi:10.1140/epjc/s10052-012-1896-2 2012

  48. [48]

    Pileup removal algorithms

    CMS Collaboration, “Pileup removal algorithms”, CMS Physics Analysis Summary CMS-PAS-JME-14-001, 2014

    2014

  49. [49]

    Pileup per particle identification

    D. Bertolini, P . Harris, M. Low, and N. Tran, “Pileup per particle identification”,JHEP10 (2014) 059,doi:10.1007/JHEP10(2014)059,arXiv:1407.6013

    work page doi:10.1007/jhep10(2014)059 2014

  50. [50]

    Pileup mitigation at CMS in 13 TeV data

    CMS Collaboration, “Pileup mitigation at CMS in 13 TeV data”,JINST15(2020) P09018, doi:10.1088/1748-0221/15/09/P09018,arXiv:2003.00503

    work page doi:10.1088/1748-0221/15/09/p09018 2020

  51. [51]

    Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV

    CMS Collaboration, “Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV”,JINST12(2017) P02014, doi:10.1088/1748-0221/12/02/P02014,arXiv:1607.03663

    work page doi:10.1088/1748-0221/12/02/p02014 2017

  52. [52]

    Catani, Y

    S. Catani, Y. L. Dokshitzer, M. H. Seymour, and B. R. Webber, “Longitudinally invariant kT clustering algorithms for hadron hadron collisions”,Nucl. Phys. B406(1993) 187, doi:10.1016/0550-3213(93)90166-M

    work page doi:10.1016/0550-3213(93)90166-m 1993

  53. [53]

    Successive combination jet algorithm for hadron collisions

    S. D. Ellis and D. E. Soper, “Successive combination jet algorithm for hadron collisions”, Phys. Rev. D48(1993) 3160,doi:10.1103/PhysRevD.48.3160, arXiv:hep-ph/9305266

    work page doi:10.1103/physrevd.48.3160 1993

  54. [54]

    Better jet clustering algorithms

    Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber, “Better jet clustering algorithms”,JHEP08(1997) 001,doi:10.1088/1126-6708/1997/08/001, arXiv:hep-ph/9707323

    work page doi:10.1088/1126-6708/1997/08/001 1997

  55. [55]

    Wobisch and T

    M. Wobisch and T. Wengler, “Hadronization corrections to jet cross-sections in deep inelastic scattering”, inProc. Workshop on Monte Carlo Generators for HERA Physics, p. 270. 1998.arXiv:hep-ph/9907280

    work page internal anchor Pith review arXiv 1998

  56. [56]

    Soft drop

    A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, “Soft drop”,JHEP05(2014) 146, doi:10.1007/JHEP05(2014)146,arXiv:1402.2657

    work page doi:10.1007/jhep05(2014)146 2014

  57. [57]

    Jet flavour classification using DeepJet

    E. Bols et al., “Jet flavour classification using DeepJet”,JINST15(2020) P12012, doi:10.1088/1748-0221/15/12/P12012,arXiv:2008.10519

    work page doi:10.1088/1748-0221/15/12/p12012 2020

  58. [58]

    Performance of missing transverse momentum reconstruction in proton-proton collisions at √s=13 TeV using the CMS detector

    CMS Collaboration, “Performance of missing transverse momentum reconstruction in proton-proton collisions at √s=13 TeV using the CMS detector”,JINST14(2019) P07004,doi:10.1088/1748-0221/14/07/P07004,arXiv:1903.06078

    work page doi:10.1088/1748-0221/14/07/p07004 2019

  59. [59]

    Alwall, R

    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,doi:10.1007/JHEP07(2014)079,arXiv:1405.0301

    work page doi:10.1007/jhep07(2014)079 2014

  60. [60]

    On the spin and parity of a single-produced resonance at the LHC

    S. Bolognesi et al., “On the spin and parity of a single-produced resonance at the LHC”, Phys. Rev. D86(2012) 095031,doi:10.1103/PhysRevD.86.095031, arXiv:1208.4018. References 27

    work page doi:10.1103/physrevd.86.095031 2012

  61. [61]

    Navaset al.),Phys

    Particle Data Group, “Review of particle physics”,Phys. Rev. D110(2024) 030001, doi:10.1103/PhysRevD.110.030001

    work page doi:10.1103/physrevd.110.030001 2024

  62. [62]

    Sj¨ ostrand, S

    T. Sj ¨ostrand et al., “An introduction toPYTHIA8.2”,Comput. Phys. Commun.191(2015) 159,doi:10.1016/j.cpc.2015.01.024,arXiv:1410.3012

    work page doi:10.1016/j.cpc.2015.01.024 2015

  63. [63]

    Pileup subtraction using jet areas

    M. Cacciari and G. P . Salam, “Pileup subtraction using jet areas”,Phys. Lett. B659(2008) 119,doi:10.1016/j.physletb.2007.09.077,arXiv:0707.1378

    work page doi:10.1016/j.physletb.2007.09.077 2008

  64. [64]

    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,doi:10.1088/1126-6708/2004/11/040, arXiv:hep-ph/0409146

    work page internal anchor Pith review doi:10.1088/1126-6708/2004/11/040 2004

  65. [65]

    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: thePOWHEGmethod”,JHEP11(2007) 070, doi:10.1088/1126-6708/2007/11/070,arXiv:0709.2092

    work page internal anchor Pith review doi:10.1088/1126-6708/2007/11/070 2007

  66. [66]

    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: thePOWHEGbox”,JHEP06(2010) 043, doi:10.1007/JHEP06(2010)043,arXiv:1002.2581

    work page internal anchor Pith review doi:10.1007/jhep06(2010)043 2010

  67. [67]

    Higgs production via gluon fusion in thePOWHEGapproach in the SM and in the MSSM

    E. Bagnaschi, G. Degrassi, P . Slavich, and A. Vicini, “Higgs production via gluon fusion in thePOWHEGapproach in the SM and in the MSSM”,JHEP02(2012) 088, doi:10.1007/JHEP02(2012)088,arXiv:1111.2854

    work page doi:10.1007/jhep02(2012)088 2012

  68. [68]

    Higgs boson pair production at NNLO with top quark mass effects

    M. Grazzini et al., “Higgs boson pair production at NNLO with top quark mass effects”, JHEP05(2018) 059,doi:10.1007/JHEP05(2018)059,arXiv:1803.02463

    work page doi:10.1007/jhep05(2018)059 2018

  69. [69]

    Neutral Higgs boson pair production at hadron colliders: QCD corrections

    S. Dawson, S. Dittmaier, and M. Spira, “Neutral Higgs boson pair production at hadron colliders: QCD corrections”,Phys. Rev. D58(1998) 115012, doi:10.1103/PhysRevD.58.115012,arXiv:hep-ph/9805244

    work page doi:10.1103/physrevd.58.115012 1998

  70. [70]

    Higgs boson pair production at next-to-next-to-leading order in QCD

    D. de Florian and J. Mazzitelli, “Higgs boson pair production at next-to-next-to-leading order in QCD”,Phys. Rev. Lett.111(2013) 201801, doi:10.1103/PhysRevLett.111.201801,arXiv:1309.6594

    work page doi:10.1103/physrevlett.111.201801 2013

  71. [71]

    Higgs pair production at next-to-next-to-leading logarithmic accuracy at the LHC

    D. de Florian and J. Mazzitelli, “Higgs pair production at next-to-next-to-leading logarithmic accuracy at the LHC”,JHEP09(2015) 053, doi:10.1007/JHEP09(2015)053,arXiv:1505.07122

    work page doi:10.1007/jhep09(2015)053 2015

  72. [72]

    Gluon fusion into Higgs pairs at NLO QCD and the top mass scheme

    J. Baglio et al., “Gluon fusion into Higgs pairs at NLO QCD and the top mass scheme”, Eur. Phys. J. C79(2019) 459,doi:10.1140/epjc/s10052-019-6973-3, arXiv:1811.05692

    work page doi:10.1140/epjc/s10052-019-6973-3 2019

  73. [73]

    Higgs boson pair production in gluon fusion at next-to-leading order with full top-quark mass dependence

    S. Borowka et al., “Higgs boson pair production in gluon fusion at next-to-leading order with full top-quark mass dependence”,Phys. Rev. Lett.117(2016) 012001, doi:10.1103/PhysRevLett.117.012001,arXiv:1604.06447. [Erratum: doi:10.1103/PhysRevLett.117.079901]

    work page doi:10.1103/physrevlett.117.012001 2016

  74. [74]

    Threshold resummation effects in Higgs boson pair production at the LHC

    D. Y. Shao, C. S. Li, H. T. Li, and J. Wang, “Threshold resummation effects in Higgs boson pair production at the LHC”,JHEP07(2013) 169,doi:10.1007/JHEP07(2013)169, arXiv:1301.1245. 28

    work page doi:10.1007/jhep07(2013)169 2013

  75. [75]

    Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements

    CMS Collaboration, “Extraction and validation of a new set of CMSPYTHIA8 tunes from underlying-event measurements”,Eur. Phys. J. C80(2020) 4, doi:10.1140/epjc/s10052-019-7499-4,arXiv:1903.12179

    work page doi:10.1140/epjc/s10052-019-7499-4 2020

  76. [76]

    Ball, et al., JHEP04, 040 (2015)

    NNPDF Collaboration, “Parton distributions for the LHC Run II”,JHEP04(2015) 040, doi:10.1007/JHEP04(2015)040,arXiv:1410.8849

    work page doi:10.1007/jhep04(2015)040 2015

  77. [77]

    Parton distributions from high-precision collider data

    NNPDF Collaboration, “Parton distributions from high-precision collider data”,Eur. Phys. J. C77(2017) 663,doi:10.1140/epjc/s10052-017-5199-5, arXiv:1706.00428

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1140/epjc/s10052-017-5199-5 2017

  78. [78]

    GEANT4 — a simulation toolkit

    GEANT4 Collaboration, “GEANT4—a simulation toolkit”,Nucl. Instrum. Meth. A506 (2003) 250,doi:10.1016/S0168-9002(03)01368-8

    work page doi:10.1016/s0168-9002(03)01368-8 2003

  79. [79]

    Attention Is All You Need

    A. Vaswani et al., “Attention is all you need”, inProc. 31st Int. Conf. on Neural Information Processing Systems, NIPS’17, p. 6000. Curran Associates Inc., Red Hook, NY, USA, 2017. arXiv:1706.03762

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  80. [80]

    Multiscale Vision Transformers , isbn =

    H. Touvron et al., “Going deeper with image transformers”, inProc. IEEE/CVF Int. Conf. on Computer Vision (ICCV), p. 32. 2021.arXiv:2103.17239. doi:10.1109/ICCV48922.2021.00010

    work page doi:10.1109/iccv48922.2021.00010 2021

Showing first 80 references.