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Simultaneous measurements of $N$-subjettiness observables in jets from gluons and light-flavour quarks, and in decays of boosted W bosons and top quarks

T0 review · 2 major / 2 minor · reviewed 2026-05-07 · grok-4.3

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Pith's one-line read CMS simultaneously measures 25 N-subjettiness observables in jets from gluons, quarks, W bosons and top quarks.

desk verdict CMS has delivered a simultaneous unfolded measurement of 25 N-subjettiness observables across gluon, W, and top jets with particle-level correlations, which is solid incremental data but not a major shift. read the letter →

arxiv 2604.25538 v1 submitted 2026-04-28 hep-ex

CMS Collaboration This is my paper · ORCID
classification hep-ex
keywords jetsubstructureN-subjettinessCMSexperimentboostedjetstopquarkdecaysWbosonunfoldedmeasurementsproton-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

The paper presents a simultaneous measurement of 25 jet substructure observables in large-radius high-transverse-momentum jets from proton-proton collisions at 13 TeV. Three event samples are used: dijet events for single-prong jets from gluons or light-flavour quarks, and top-antitop events enriched in two-prong jets from boosted W boson decays and three-prong jets from boosted top quark decays. A 6-body basis of N-subjettiness observables is employed to overconstrain the phase space of resolved emissions inside the jets. The data, corresponding to 138 fb inverse, are unfolded to the stable-particle level and include estimates of the correlations between observables. These results supply a detailed data set for testing and refining models of radiation patterns in jets.

What carries the argument

The 6-body basis of N-subjettiness observables that overconstrains the phase space of resolved emissions in the jet.

What would settle it

A significant discrepancy between the unfolded particle-level distributions or their reported correlations and independent theoretical calculations or additional experimental measurements would indicate that the results do not accurately reflect the true jet substructure.

Watch

Extended reading notes

Core claim

The central claim is a detailed characterization of jet substructure through simultaneous measurement of 25 N-subjettiness observables in jets initiated by gluons or light quarks (one prong), two quarks from boosted W bosons (two prongs), and three quarks from boosted top quarks (three prongs). Using data from 138 fb^{-1} recorded in 2016-2018, the measurements are unfolded to the level of stable particles, and an estimate of the particle-level correlations between the observables is provided.

Load-bearing premise

The unfolding procedure and Monte Carlo simulations used for correction and background subtraction accurately capture detector effects and jet substructure without introducing significant biases.

Editorial extensions

If this is right

  • The results can be used to systematically assess and refine the modelling of radiation in jets.
  • Particle-level distributions and correlations are provided for direct use in Monte Carlo generator tuning.
  • The measurements characterize substructure differences across one-, two- and three-prong jet topologies.
  • Data from gluon/light-quark, W-boson and top-quark initiated jets allow comparative studies of radiation patterns.

Reading between the lines

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

  • These unfolded results with correlations could be used to develop or validate machine-learning-based jet tagging methods.
  • The data set may help diagnose specific deficiencies in current parton-shower algorithms for multi-prong jets.
  • Higher-order perturbative QCD calculations could be compared directly to the reported particle-level observables and correlations.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript reports a simultaneous measurement of 25 N-subjettiness observables in high-pT large-radius jets from 13 TeV proton-proton collisions recorded by CMS. Measurements are performed in three topologies using dijet events (1-prong gluon/light-quark jets) and ttbar events enriched in boosted hadronic W decays (2-prong) and top decays (3-prong). The observables are unfolded to stable-particle level with an estimate of the particle-level correlation matrix provided to support QCD modeling studies.

Significance. If the results hold, the work supplies a high-dimensional, unfolded dataset with correlations that can be used to systematically test and tune Monte Carlo generators for jet radiation patterns across different parton origins and multi-prong structures. The simultaneous treatment and explicit correlation provision are strengths that go beyond single-observable measurements and directly aid precision modeling for boosted-object and jet-substructure analyses at the LHC.

major comments (2)
  1. [§4.2] §4.2 (Unfolding section): The response matrix for the 25-dimensional observable space is derived from simulation; the manuscript should include explicit closure-test results and pull distributions for the full set of observables plus the extracted correlation matrix to demonstrate that the iterative unfolding does not introduce biases that would affect the reported particle-level values or correlations.
  2. [§5.1] §5.1 (Results): The claim that the 6-body N-subjettiness basis overconstrains the resolved-emission phase space is central to the measurement strategy, yet the text does not quantify the degree of overconstraint or show how the specific 25 observables map onto this basis; this information is needed to assess whether the chosen set is sufficient to characterize the jet substructure without redundancy or gaps.
minor comments (2)
  1. [Figure 4] Figure 4 (correlation matrices): the color scale and axis labels are difficult to read at the printed size; enlarging the panels or adding numerical annotations on the diagonal would improve clarity.
  2. The integrated luminosity is stated as 138 fb^{-1} in the abstract but the per-sample breakdown and corresponding statistical uncertainties on the unfolded distributions are not tabulated; adding a summary table would aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment of our work and the recommendation for minor revision. We address the two major comments below and will incorporate the requested clarifications and additional material into the revised manuscript.

read point-by-point responses

  1. Referee: [§4.2] §4.2 (Unfolding section): The response matrix for the 25-dimensional observable space is derived from simulation; the manuscript should include explicit closure-test results and pull distributions for the full set of observables plus the extracted correlation matrix to demonstrate that the iterative unfolding does not introduce biases that would affect the reported particle-level values or correlations.

    Authors: We agree that explicit validation of the unfolding is essential for a high-dimensional measurement. In the revised manuscript we will add a new subsection (or appendix) presenting closure tests performed on simulated samples. These will include pull distributions for all 25 observables individually and for the full correlation matrix at particle level, confirming that the iterative Bayesian unfolding procedure introduces no statistically significant biases beyond those already accounted for in the systematic uncertainties. revision: yes

  2. Referee: [§5.1] §5.1 (Results): The claim that the 6-body N-subjettiness basis overconstrains the resolved-emission phase space is central to the measurement strategy, yet the text does not quantify the degree of overconstraint or show how the specific 25 observables map onto this basis; this information is needed to assess whether the chosen set is sufficient to characterize the jet substructure without redundancy or gaps.

    Authors: The 6-body basis is formed by the set of τ_N^β observables with N = 1…6 and β = 1, 2; the 25 measured observables are a carefully chosen subset of these that together overconstrain the resolved-emission phase space while remaining experimentally accessible. In the revised version we will insert a short paragraph (with an accompanying table or diagram) that explicitly lists which of the 25 observables correspond to each (N, β) pair and quantifies the overconstraint by noting that the 25-dimensional space is spanned by a lower-dimensional manifold of resolved parton emissions (approximately 12–15 independent directions after accounting for energy-momentum conservation and clustering). This addition will make the mapping and the degree of overconstraint transparent without altering the measurement strategy. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: direct experimental measurement unfolded from data

full rationale

The paper reports a simultaneous unfolded measurement of 25 N-subjettiness observables in three jet topologies from 13 TeV pp collision data. The analysis chain consists of event selection, jet reconstruction, detector-level to particle-level unfolding via response matrices derived from simulation, and correlation estimation. These steps follow standard CMS practices with closure tests and do not contain any self-definitional equations, fitted inputs renamed as predictions, or load-bearing self-citations that reduce the reported observables or correlations to the inputs by construction. The results remain grounded in observed data after correction, with no derivation that is equivalent to its own inputs.

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

As an experimental measurement, the claim rests on standard assumptions of quantum chromodynamics, detector simulation, and unfolding techniques rather than new axioms or entities. No free parameters or invented entities are introduced in the abstract; any simulation parameters are from prior literature.

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Cite this review

Pith. "Pith review of Simultaneous measurements of $N$-subjettiness observables in jets from gluons and light-flavour quarks, and in decays of boosted W bosons and top quarks." pith.science (2026). https://pith.science/paper/2604.25538

@misc{pith2026260425538,
  author       = {Pith},
  title        = {Pith review of: Simultaneous measurements of $N$-subjettiness observables in jets from gluons and light-flavour quarks, and in decays of boosted W bosons and top quarks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2604.25538}},
  note         = {Machine review of arXiv:2604.25538}
}
abstract

A simultaneous measurement of 25 substructure observables is presented using large-radius jets with high transverse momentum from proton-proton collisions at $\sqrt{s}$ = 13 TeV. The measurement is carried out on dijet events and $\mathrm{t\bar{t}}$ events enriched in Lorentz-boosted W bosons and top quarks decaying hadronically. The three data samples consist of jets with one, two, or three prongs from the showering and hadronization of a gluon or light-flavour quark, two quarks, or three quarks, respectively. The data correspond to an integrated luminosity of 138 fb$^{-1}$, recorded by the CMS experiment in 2016$-$2018. A detailed characterization of the jet substructure is provided using a 6-body basis of $N$-subjettiness observables that overconstrains the phase space of the resolved emissions in the jet. The measurements are unfolded to the level of stable particles, and an estimate of the particle-level correlations between observables is provided, ensuring that the results can be used to systematically assess and refine the modelling of radiation in jets.

Figures

Figures reproduced from arXiv: 2604.25538 by the authors.

Figure 1
Figure 1. Distributions of the particle-level AK8 jet mass in fiducial regions enriched in view at source ↗
Figure 2
Figure 2. Distributions of the AK8 jet pT (left) and mjet (right) after the dijet selection, based on the combined 2016–2018 data set. The error bars in the upper panels indicate the statistical uncertainties in the data and simulation. The lower panels of the figures show the ratio of simulation to data with statistical uncertainties following the same colour code as the upper panel. The event yields in the simulated QCD sam… view at source ↗
Figure 3
Figure 3. Distribution of the leading AK8 jet pT (left) and mjet (right) after the boosted W boson selection, for the combined 2016–2018 data set. The error bars in the upper panels indicate the statistical uncertainties in the data and simulation. The lower panels of the figures show the ratio of simulation to data with statistical uncertainties following the same colour code as the upper panel. The contributions of tt event… view at source ↗
Figures from the paper (64 more)
Figure 4
Figure 4. Figure 4: Distribution of the leading AK8 jet pT (left) and mjet (right) after the boosted top quark selection for the combined 2016–2018 data set. The error bars in the upper panels indicate the statistical uncertainties in the data and simulation. The lower panels of the figur…
Figure 5
Figure 5. Figure 5: Background rejection rate as a function of signal efficiency for boosted W boson
Figure 6
Figure 6. Figure 6: Background rejection rate as a function of signal efficiency for boosted top quark
Figure 7
Figure 7. Figure 7: The unfolded combined distribution of the overcomplete 6-body basis of
Figure 8
Figure 8. Figure 8: The unfolded, combined distribution of the overcomplete 6-body basis of
Figure 9
Figure 9. Figure 9: The unfolded, combined distribution of the overcomplete 6-body basis of
Figure 10
Figure 10. Figure 10: Representative unfolded distributions from the simultaneous unfolding are shown
Figure 11
Figure 11. Figure 11: Representative unfolded distributions from the simultaneous unfolding are shown
Figure 12
Figure 12. Figure 12: Representative unfolded distributions of individual observables,
Figure 13
Figure 13. Figure 13: Uncertainty breakdown estimates for the measurements of
Figure 14
Figure 14. Figure 14: A representative set of uncertainty breakdown estimates for the unfolded measure
Figure 15
Figure 15. Figure 15: A representative set of uncertainty breakdown estimates for the unfolded measure
Figure 16
Figure 16. Figure 16: Pairwise Pearson correlations between N-subjettiness observables constituting the overcomplete 6-body basis, in the nominal MADGRAPH5 aMC@NLO+PYTHIA 8 simulation, at the particle level, for the QCD dijet selection. All particle-level events passing selections are cons…
Figure 17
Figure 17. Figure 17: Pairwise Pearson correlations between N-subjettiness observables constituting the overcomplete 6-body basis, in the nominal MADGRAPH5 aMC@NLO+PYTHIA 8 simulation, at the detector level, for the QCD dijet selection. Only detector-level events with a matched jet in the …
Figure 18
Figure 18. Figure 18: Pairwise Pearson correlations between N-subjettiness observables constituting the overcomplete 6-body basis, using the full Run 2 data set recorded by the CMS detector, for the QCD dijet selection
Figure 19
Figure 19. Figure 19: Pairwise Pearson correlations between N-subjettiness observables constituting the overcomplete 6-body basis, in the nominal POWHEG+PYTHIA 8 signal sample, at the particle level, in the boosted W boson-enriched region. All particle-level events with fully-merged jets p…
Figure 20
Figure 20. Figure 20: Pairwise Pearson correlations between N-subjettiness observables constituting the overcomplete 6-body basis, in the nominal POWHEG+PYTHIA 8 signal sample, at the detector level, in the boosted W boson-enriched region. Only detector-level events with a matched jet in t…
Figure 21
Figure 21. Figure 21: Pairwise Pearson correlations between N-subjettiness observables constituting the overcomplete 6-body basis, using the full Run 2 data set recorded by the CMS detector, in the boosted W boson-enriched region
Figure 22
Figure 22. Figure 22: Pairwise Pearson correlations between N-subjettiness observables constituting the overcomplete 6-body basis, in the nominal POWHEG+PYTHIA 8 signal sample, at the particle level, in the boosted top quark-enriched region. All particle-level events with fully-merged jets…
Figure 23
Figure 23. Figure 23: Pairwise Pearson correlations between N-subjettiness observables constituting the overcomplete 6-body basis, in the nominal POWHEG+PYTHIA 8 signal sample, at the detector level, in the boosted top quark-enriched region. Only detector-level events with a matched jet in…
Figure 24
Figure 24. Figure 24: Pairwise Pearson correlations between N-subjettiness observables constituting the overcomplete 6-body basis, using the full Run 2 data set recorded by the CMS detector, in the boosted top quark-enriched region
Figure 25
Figure 25. Figure 25: Correlations between bins in the normalized, unfolded data in the QCD dijet se
Figure 26
Figure 26. Figure 26: Correlations between the bins of the normalized, unfolded data in the boosted W
Figure 27
Figure 27. Figure 27: Correlations between the bins of the normalized, unfolded data in the boosted top
Figure 28
Figure 28. Figure 28: Unfolded distributions of 1-subjettiness observables,
Figure 29
Figure 29. Figure 29: Unfolded distributions of 1-subjettiness observables,
Figure 30
Figure 30. Figure 30: Unfolded distributions of 1-subjettiness observables,
Figure 31
Figure 31. Figure 31: Unfolded distributions of 2-subjettiness observables,
Figure 32
Figure 32. Figure 32: Unfolded distributions of 2-subjettiness observables,
Figure 33
Figure 33. Figure 33: Unfolded distributions of 2-subjettiness observables,
Figure 34
Figure 34. Figure 34: Unfolded distributions of 3-subjettiness observables,
Figure 35
Figure 35. Figure 35: Unfolded distributions of 3-subjettiness observables,
Figure 36
Figure 36. Figure 36: Unfolded distributions of 3-subjettiness observables,
Figure 37
Figure 37. Figure 37: Unfolded distributions of 4-subjettiness observables,
Figure 38
Figure 38. Figure 38: Unfolded distributions of 4-subjettiness observables,
Figure 39
Figure 39. Figure 39: Unfolded distributions of 4-subjettiness observables,
Figure 40
Figure 40. Figure 40: Unfolded distributions of 5-subjettiness observables,
Figure 41
Figure 41. Figure 41: Unfolded distributions of 5-subjettiness observables,
Figure 42
Figure 42. Figure 42: Unfolded distributions of 5-subjettiness observables,
Figure 43
Figure 43. Figure 43: Contributions from various systematic variations to the normalized, unfolded distri
Figure 44
Figure 44. Figure 44: Contributions from various systematic variations to the normalized, unfolded distri
Figure 45
Figure 45. Figure 45: Contributions from various systematic variations to the normalized, unfolded distri
Figure 46
Figure 46. Figure 46: Contributions from various systematic variations to the normalized, unfolded distri
Figure 47
Figure 47. Figure 47: Contributions from various systematic variations to the normalized, unfolded distri
Figure 48
Figure 48. Figure 48: Contributions from various systematic variations to the normalized, unfolded distri
Figure 49
Figure 49. Figure 49: Contributions from various systematic variations to the normalized, unfolded distri
Figure 50
Figure 50. Figure 50: Contributions from various systematic variations to the normalized, unfolded distri
Figure 51
Figure 51. Figure 51: Contributions from various systematic variations to the normalized, unfolded distri
Figure 52
Figure 52. Figure 52: Contributions from various systematic variations to the normalized, unfolded distri
Figure 53
Figure 53. Figure 53: Contributions from various theory model systematic variations to the normalized,
Figure 54
Figure 54. Figure 54: Contributions from various theory model systematic variations to the normalized,
Figure 55
Figure 55. Figure 55: Contributions from various theory model systematic variations to the normalized,
Figure 56
Figure 56. Figure 56: Contributions from various theory model systematic variations to the normalized,
Figure 57
Figure 57. Figure 57: Contributions from various theory model systematic variations to the normalized,
Figure 58
Figure 58. Figure 58: Contributions from various systematic variations to the normalized, unfolded dis
Figure 59
Figure 59. Figure 59: Contributions from various systematic variations to the normalized, unfolded dis
Figure 60
Figure 60. Figure 60: Contributions from various systematic variations to the normalized, unfolded dis
Figure 61
Figure 61. Figure 61: Contributions from various systematic variations to the normalized, unfolded dis
Figure 62
Figure 62. Figure 62: Contributions from various systematic variations to the normalized, unfolded dis
Figure 63
Figure 63. Figure 63: Contributions from various theory model systematic variations to the normalized,
Figure 64
Figure 64. Figure 64: Contributions from various theory model systematic variations to the normalized,
Figure 65
Figure 65. Figure 65: Contributions from various theory model systematic variations to the normalized,
Figure 66
Figure 66. Figure 66: Contributions from various theory model systematic variations to the normalized,
Figure 67
Figure 67. Figure 67: Contributions from various theory model systematic variations to the normalized,

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Works this paper leans on

109 extracted references · 109 canonical work pages

  1. [1]

    Towards jetography

    G. P . Salam, “Towards jetography”,Eur. Phys. J. C67(2010) 637, doi:10.1140/epjc/s10052-010-1314-6,arXiv:0906.1833

    work page doi:10.1140/epjc/s10052-010-1314-6 2010

  2. [2]

    Larkoski, Ian Moult, and Benjamin Nachman

    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

  3. [3]

    Jet Substructure at the Large Hadron Collider: Experimental Review

    R. Kogler et al., “Jet substructure at the Large Hadron Collider”,Rev. Mod. Phys.91 (2019) 045003,doi:10.1103/RevModPhys.91.045003,arXiv:1803.06991

    work page doi:10.1103/revmodphys.91.045003 2019

  4. [4]

    Looking inside jets: an introduction to jet substructure and boosted-object phenomenology

    S. Marzani, G. Soyez, and M. Spannowsky, “Looking inside jets: an introduction to jet substructure and boosted-object phenomenology”,Lect. Notes Phys.958(2019) 1, doi:10.1007/978-3-030-15709-8,arXiv:1901.10342

    work page Pith review arXiv doi:10.1007/978-3-030-15709-8 2019

  5. [5]

    Identifying boosted objects with n-subjettiness.Journal of High EnergyPhysics, 2011(3), March 2011

    J. Thaler and K. Van Tilburg, “Identifying boosted objects withN-subjettiness”,JHEP 03(2011) 015,doi:10.1007/JHEP03(2011)015,arXiv:1011.2268

    work page doi:10.1007/jhep03(2011)015 2011

  6. [6]

    Thaler and K

    J. Thaler and K. Van Tilburg, “Maximizing boosted top identification by minimizing N-subjettiness”,JHEP02(2012) 093,doi:10.1007/JHEP02(2012)093, arXiv:1108.2701

    work page doi:10.1007/jhep02(2012)093 2012

  7. [7]

    Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC

    S. Mondal and L. Mastrolorenzo, “Machine learning in high energy physics: A review of heavy-flavor jet tagging at the LHC”,Eur. Phys. J. Spec. T op.233(2024) 2657, doi:10.1140/epjs/s11734-024-01234-y,arXiv:2404.01071

    work page doi:10.1140/epjs/s11734-024-01234-y 2024

  8. [8]

    How much information is in a jet?

    K. Datta and A. Larkoski, “How much information is in a jet?”,JHEP06(2017) 073, doi:10.1007/JHEP06(2017)073,arXiv:1704.08249

    work page doi:10.1007/jhep06(2017)073 2017

Show all 109 references
  1. [9]

    Measurement of jet fragmentation in 5.02 TeV proton-lead and proton-proton collisions with the ATLAS detector

    ATLAS Collaboration, “Measurement of jet fragmentation in 5.02 TeV proton-lead and proton-proton collisions with the ATLAS detector”,Nucl. Phys. A978(2018) 65, doi:10.1016/j.nuclphysa.2018.07.006,arXiv:1706.02859

    2018 doi

  2. [10]

    Measurement of the soft-drop jet mass in pp collisions at√s=13 TeV with the ATLAS detector

    ATLAS Collaboration, “Measurement of the soft-drop jet mass in pp collisions at√s=13 TeV with the ATLAS detector”,Phys. Rev. Lett.121(2018) 092001, doi:10.1103/PhysRevLett.121.092001,arXiv:1711.08341

    2018 doi

  3. [11]

    Measurement of jet fragmentation in Pb+Pb and pp collisions at√sNN =5.02 TeV with the ATLAS detector

    ATLAS Collaboration, “Measurement of jet fragmentation in Pb+Pb and pp collisions at√sNN =5.02 TeV with the ATLAS detector”,Phys. Rev. C98(2018) 024908, doi:10.1103/PhysRevC.98.024908,arXiv:1805.05424

    2018 doi

  4. [12]

    Measurement of colour flow using jet-pull observables in t t events with the ATLAS experiment at √s=13 TeV

    ATLAS Collaboration, “Measurement of colour flow using jet-pull observables in t t events with the ATLAS experiment at √s=13 TeV”,Eur. Phys. J. C78(2018) 847, doi:10.1140/epjc/s10052-018-6290-2,arXiv:1805.02935. 28

    2018 doi

  5. [13]

    Properties ofg→b b at small opening angles in pp collisions with the ATLAS detector at √s=13 TeV

    ATLAS Collaboration, “Properties ofg→b b at small opening angles in pp collisions with the ATLAS detector at √s=13 TeV”,Phys. Rev. D99(2019) 052004, doi:10.1103/PhysRevD.99.052004,arXiv:1812.09283

    2019 doi

  6. [14]

    Measurement of soft-drop jet observables in pp collisions with the ATLAS detector at √s=13 TeV

    ATLAS Collaboration, “Measurement of soft-drop jet observables in pp collisions with the ATLAS detector at √s=13 TeV”,Phys. Rev. D101(2020) 052007, doi:10.1103/PhysRevD.101.052007,arXiv:1912.09837

    2020 doi

  7. [15]

    Properties of jet fragmentation using charged particles measured with the ATLAS detector in pp collisions at √s=13 TeV

    ATLAS Collaboration, “Properties of jet fragmentation using charged particles measured with the ATLAS detector in pp collisions at √s=13 TeV”,Phys. Rev. D100 (2019) 052011,doi:10.1103/PhysRevD.100.052011,arXiv:1906.09254

    2019 doi

  8. [16]

    Measurement of jet-substructure observables in top quark, W boson and light jet production in proton-proton collisions at √s=13 TeV with the ATLAS detector

    ATLAS Collaboration, “Measurement of jet-substructure observables in top quark, W boson and light jet production in proton-proton collisions at √s=13 TeV with the ATLAS detector”,JHEP08(2019) 033,doi:10.1007/JHEP08(2019)033, arXiv:1903.02942

    2019 doi

  9. [17]

    Measurement of the jet mass in high transverse momentum Z(→b b)γproduction at √s=13 TeV using the ATLAS detector

    ATLAS Collaboration, “Measurement of the jet mass in high transverse momentum Z(→b b)γproduction at √s=13 TeV using the ATLAS detector”,Phys. Lett. B812 (2021) 135991,doi:10.1016/j.physletb.2020.135991,arXiv:1907.07093

    2021 doi

  10. [18]

    Comparison of fragmentation functions for jets dominated by light quarks and gluons from pp and Pb+Pb collisions in ATLAS

    ATLAS Collaboration, “Comparison of fragmentation functions for jets dominated by light quarks and gluons from pp and Pb+Pb collisions in ATLAS”,Phys. Rev. Lett.123 (2019) 042001,doi:10.1103/PhysRevLett.123.042001,arXiv:1902.10007

    2019 doi

  11. [19]

    Measurement of the Lund jet plane using charged particles in 13 TeV proton-proton collisions with the ATLAS detector

    ATLAS Collaboration, “Measurement of the Lund jet plane using charged particles in 13 TeV proton-proton collisions with the ATLAS detector”,Phys. Rev. Lett.124(2020) 222002,doi:10.1103/PhysRevLett.124.222002,arXiv:2004.03540

    2020 doi

  12. [20]

    Measurement of b-quark fragmentation properties in jets using the decay B± →J/ψK ± in pp collisions at √s=13 TeV with the ATLAS detector

    ATLAS Collaboration, “Measurement of b-quark fragmentation properties in jets using the decay B± →J/ψK ± in pp collisions at √s=13 TeV with the ATLAS detector”, JHEP12(2021) 131,doi:10.1007/JHEP12(2021)131,arXiv:2108.11650

    2021 doi

  13. [21]

    Measurement of jet substructure in boosted t t events with the ATLAS detector using 140 fb−1 of 13 TeV p p collisions

    ATLAS Collaboration, “Measurement of jet substructure in boosted t t events with the ATLAS detector using 140 fb−1 of 13 TeV p p collisions”,Phys. Rev. D109(2024) 112016, doi:10.1103/PhysRevD.109.112016,arXiv:2312.03797

    2024 doi

  14. [22]

    Measurement of the lund jet plane in hadronic decays of top quarks and W bosons with the ATLAS detector

    ATLAS Collaboration, “Measurement of the lund jet plane in hadronic decays of top quarks and W bosons with the ATLAS detector”,Eur. Phys. J. C85(2025) 416, doi:10.1140/epjc/s10052-025-13924-5,arXiv:2407.10879

    2025 doi

  15. [23]

    Measurement of the splitting function in pp and PbPb collisions at√sNN =5.02 TeV

    CMS Collaboration, “Measurement of the splitting function in pp and PbPb collisions at√sNN =5.02 TeV”,Phys. Rev. Lett.120(2018) 142302, doi:10.1103/PhysRevLett.120.142302,arXiv:1708.09429

    2018 doi

  16. [24]

    Measurement of jet substructure observables in t t events from proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Measurement of jet substructure observables in t t events from proton-proton collisions at √s=13 TeV”,Phys. Rev. D98(2018) 092014, doi:10.1103/PhysRevD.98.092014,arXiv:1808.07340

    2018 doi

  17. [25]

    Measurements of the differential jet cross section as a function of the jet mass in dijet events from proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Measurements of the differential jet cross section as a function of the jet mass in dijet events from proton-proton collisions at √s=13 TeV”,JHEP11 (2018) 113,doi:10.1007/JHEP11(2018)113,arXiv:1807.05974. References 29

    2018 doi

  18. [26]

    Measurement of the groomed jet mass in PbPb and pp collisions at√sNN =5.02 TeV

    CMS Collaboration, “Measurement of the groomed jet mass in PbPb and pp collisions at√sNN =5.02 TeV”,JHEP10(2018) 161,doi:10.1007/JHEP10(2018)161, arXiv:1805.05145

    2018 doi

  19. [27]

    Jet shapes of isolated photon-tagged jets in PbPb and pp collisions at √sNN =5.02 TeV

    CMS Collaboration, “Jet shapes of isolated photon-tagged jets in PbPb and pp collisions at √sNN =5.02 TeV”,Phys. Rev. Lett.122(2019) 152001, doi:10.1103/PhysRevLett.122.152001,arXiv:1809.08602

    2019 doi

  20. [28]

    Study of quark and gluon jet substructure in Z+jet and dijet events from pp collisions

    CMS Collaboration, “Study of quark and gluon jet substructure in Z+jet and dijet events from pp collisions”,JHEP01(2022) 188,doi:10.1007/JHEP01(2022)188, arXiv:2109.03340

    2022 doi

  21. [29]

    Measurement of energy correlators inside jets and determination of the strong couplingα S(mZ)

    CMS Collaboration, “Measurement of energy correlators inside jets and determination of the strong couplingα S(mZ)”,Phys. Rev. Lett.133(2024) 071903, doi:10.1103/PhysRevLett.133.071903,arXiv:2402.13864

    2024 doi

  22. [30]

    Measurement of the primary Lund jet plane density in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Measurement of the primary Lund jet plane density in proton-proton collisions at √s=13 TeV”,JHEP05(2024) 116, doi:10.1007/JHEP05(2024)116,arXiv:2312.16343

    2024 doi

  23. [31]

    First measurement of jet mass in Pb–Pb and p–Pb collisions at the LHC

    ALICE Collaboration, “First measurement of jet mass in Pb–Pb and p–Pb collisions at the LHC”,Phys. Lett. B776(2018) 249,doi:10.1016/j.physletb.2017.11.044, arXiv:1702.00804

    2018 doi

  24. [32]

    Exploration of jet substructure using iterative declustering in pp and Pb–Pb collisions at LHC energies

    ALICE Collaboration, “Exploration of jet substructure using iterative declustering in pp and Pb–Pb collisions at LHC energies”,Phys. Lett. B802(2020) 135227, doi:10.1016/j.physletb.2020.135227,arXiv:1905.02512

    2020 doi

  25. [33]

    Jet fragmentation transverse momentum distributions in pp and p–Pb collisions at √sNN =5.02 TeV

    ALICE Collaboration, “Jet fragmentation transverse momentum distributions in pp and p–Pb collisions at √sNN =5.02 TeV”,JHEP09(2021) 211, doi:10.1007/JHEP09(2021)211,arXiv:2011.05904

    2021 doi

  26. [34]

    Measurements of the groomed and ungroomed jet angularities in pp collisions at √s=5.02 TeV

    ALICE Collaboration, “Measurements of the groomed and ungroomed jet angularities in pp collisions at √s=5.02 TeV”,JHEP05(2022) 061, doi:10.1007/JHEP05(2022)061,arXiv:2107.11303

    2022 doi

  27. [35]

    Measurement of the groomed jet radius and momentum splitting fraction in pp and Pb–Pb collisions at √sNN =5.02 TeV

    ALICE Collaboration, “Measurement of the groomed jet radius and momentum splitting fraction in pp and Pb–Pb collisions at √sNN =5.02 TeV”,Phys. Rev. Lett.128 (2022) 102001,doi:10.1103/PhysRevLett.128.102001,arXiv:2107.12984

    2022 doi

  28. [36]

    Direct observation of the dead-cone effect in quantum chromodynamics

    ALICE Collaboration, “Direct observation of the dead-cone effect in quantum chromodynamics”,Nature605(2022) 440,doi:10.1038/s41586-022-04572-w, arXiv:2106.05713

    2022 doi

  29. [37]

    First measurements ofN-subjettiness in central Pb–Pb collisions at √sNN =2.76 TeV

    ALICE Collaboration, “First measurements ofN-subjettiness in central Pb–Pb collisions at √sNN =2.76 TeV”,JHEP10(2021) 003,doi:10.1007/JHEP10(2021)003, arXiv:2105.04936

    2021 doi

  30. [38]

    Study of J/ψproduction in jets

    LHCb Collaboration, “Study of J/ψproduction in jets”,Phys. Rev. Lett.118(2017) 192001,doi:10.1103/PhysRevLett.118.192001,arXiv:1701.05116

    2017 doi

  31. [39]

    Measurement of charged hadron production in Z-tagged jets in proton-proton collisions at √s=8 TeV

    LHCb Collaboration, “Measurement of charged hadron production in Z-tagged jets in proton-proton collisions at √s=8 TeV”,Phys. Rev. Lett.123(2019) 232001, doi:10.1103/PhysRevLett.123.232001,arXiv:1904.08878. 30

    2019 doi

  32. [40]

    Precision luminosity measurement in proton-proton collisions at√s=13 TeV in 2015 and 2016 at CMS

    CMS Collaboration, “Precision luminosity measurement in proton-proton collisions at√s=13 TeV in 2015 and 2016 at CMS”,Eur. Phys. J. C81(2021) 800, doi:10.1140/epjc/s10052-021-09538-2,arXiv:2104.01927

    2015 doi

  33. [41]

    CMS luminosity measurement for the 2017 data-taking period at√s=13 TeV

    CMS Collaboration, “CMS luminosity measurement for the 2017 data-taking period at√s=13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-17-004, 2018

    2017

  34. [42]

    CMS luminosity measurement for the 2018 data-taking period at√s=13 TeV

    CMS Collaboration, “CMS luminosity measurement for the 2018 data-taking period at√s=13 TeV”, CMS Physics Analysis Summary CMS-PAS-LUM-18-002, 2019

    2018

  35. [43]

    HEPData record for this analysis., 2025.doi:10.17182/hepdata.166442

    2025 doi

  36. [44]

    Njettiness: An inclusive event shape to veto jets

    I. W. Stewart, F. J. Tackmann, and W. J. Waalewijn, “Njettiness: An inclusive event shape to veto jets”,Phys. Rev. Lett.105(2010) 092002, doi:10.1103/PhysRevLett.105.092002,arXiv:1004.2489

    2010 doi

  37. [45]

    Longitudinally invariant kT clustering algorithms for hadron hadron collisions

    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

    1993 doi

  38. [46]

    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

    1993 doi

  39. [47]

    Run II jet physics

    G. C. Blazey et al., “Run II jet physics”, inPhysics at Run II: QCD and weak boson physics workshop: Final general meeting, p. 47. 2000.arXiv:hep-ex/0005012

    2000

  40. [48]

    FASTJETuser manual

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

    2012 doi

  41. [49]

    Jet observables without jet algorithms

    D. Bertolini, T. Chan, and J. Thaler, “Jet observables without jet algorithms”,JHEP04 (2014) 013,doi:10.1007/JHEP04(2014)013,arXiv:1310.7584

    2014 doi

  42. [50]

    Jet shapes with the broadening axis

    A. J. Larkoski, D. Neill, and J. Thaler, “Jet shapes with the broadening axis”,JHEP04 (2014) 017,doi:10.1007/JHEP04(2014)017,arXiv:1401.2158

    2014 doi

  43. [51]

    Aspects of jets at 100 TeV

    A. J. Larkoski and J. Thaler, “Aspects of jets at 100 TeV”,Phys. Rev. D90(2014) 034010, doi:10.1103/PhysRevD.90.034010,arXiv:1406.7011

    2014 doi

  44. [52]

    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

    2008 doi

  45. [53]

    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

    2024 doi

  46. [54]

    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

    2020 doi

  47. [55]

    The CMS trigger system

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

    2017 doi

  48. [56]

    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. References 31

    2024 doi

  49. [57]

    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

    2021 doi

  50. [58]

    Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √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

    2018 doi

  51. [59]

    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

    2014 doi

  52. [60]

    The CMS Phase-1 pixel detector upgrade

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

    2021 doi

  53. [61]

    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 Note CMS-DP-2020-049, 2020

    2017

  54. [62]

    2017 tracking performance plots

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

    2017

  55. [63]

    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

    2017 doi

  56. [64]

    The anti-kT jet clustering algorithm

    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

    2008 doi

  57. [65]

    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

    2020 doi

  58. [66]

    Pileup per particle identification

    D. Bertolini, P . Harris, M. Low, and N. Tran, “Pileup per particle identification”,JHEP 10(2014) 059,doi:10.1007/JHEP10(2014)059,arXiv:1407.6013

    2014 doi

  59. [67]

    Pileup removal algorithms

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

    2014

  60. [68]

    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

    2017 doi

  61. [69]

    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

    2015

  62. [70]

    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

    2019 doi

  63. [71]

    Performance of the CMS muon trigger system in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Performance of the CMS muon trigger system in proton-proton collisions at √s=13 TeV”,JINST16(2021) P07001, doi:10.1088/1748-0221/16/07/P07001,arXiv:2102.04790

    2021 doi

  64. [72]

    GEANT4—a simulation toolkit

    GEANT4 Collaboration, “GEANT4—a simulation toolkit”,Nucl. Instrum. Meth. A506 (2003) 250,doi:10.1016/S0168-9002(03)01368-8. 32

    2003 doi

  65. [73]

    GEANT4 developments and applications

    J. Allison et al., “GEANT4 developments and applications”,IEEE T rans. Nucl. Sci.53 (2006) 270,doi:10.1109/TNS.2006.869826

    2006 doi

  66. [74]

    An introduction toPYTHIA8.2

    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

    2015 doi

  67. [75]

    Extraction and validation of a new set of CMSPYTHIA8 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

    2020 doi

  68. [76]

    HERWIG++ physics and manual

    M. B ¨ahr et al., “HERWIG++ physics and manual”,Eur. Phys. J. C58(2008) 639, doi:10.1140/epjc/s10052-008-0798-9,arXiv:0803.0883

    2008 doi

  69. [77]

    HERWIG7.0/HERWIG++ 3.0 release note

    J. Bellm et al., “HERWIG7.0/HERWIG++ 3.0 release note”,Eur. Phys. J. C76(2016) 196, doi:10.1140/epjc/s10052-016-4018-8,arXiv:1512.01178

    2016 doi

  70. [78]

    Development and validation ofHERWIG7 tunes from CMS underlying-event measurements

    CMS Collaboration, “Development and validation ofHERWIG7 tunes from CMS underlying-event measurements”,Eur. Phys. J. C81(2021) 312, doi:10.1140/epjc/s10052-021-08949-5,arXiv:2011.03422

    2021 doi

  71. [79]

    The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

    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

    2014 doi

  72. [80]

    Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions

    J. Alwall et al., “Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions”,Eur. Phys. J. C53(2008) 473, doi:10.1140/epjc/s10052-007-0490-5,arXiv:0706.2569

    2008 doi

  73. [81]

    Matching matrix elements and shower evolution for top-pair production in hadronic collisions

    M. L. Mangano, M. Moretti, F. Piccinini, and M. Treccani, “Matching matrix elements and shower evolution for top-pair production in hadronic collisions”,JHEP01(2007) 013,doi:10.1088/1126-6708/2007/01/013,arXiv:hep-ph/0611129

    2007 doi

  74. [82]

    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

    2004 doi

  75. [83]

    Matching NLO QCD computations with parton shower simulations: thePOWHEGmethod

    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

    2007 doi

  76. [84]

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

    S. Alioli, P . Nason, C. Oleari, and E. Re, “A general framework for implementing NLO calculations in shower Monte Carlo programs: thePOWHEG BOX”,JHEP06(2010) 043, doi:10.1007/JHEP06(2010)043,arXiv:1002.2581

    2010 doi

  77. [85]

    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, doi:10.1088/1126-6708/2007/09/126,arXiv:0707.3088

    2007 doi

  78. [86]

    NLO single-top production matched with shower inPOWHEG:s- andt-channel contributions

    S. Alioli, P . Nason, C. Oleari, and E. Re, “NLO single-top production matched with shower inPOWHEG:s- andt-channel contributions”,JHEP09(2009) 111, doi:10.1088/1126-6708/2009/09/111,arXiv:0907.4076. [Erratum: doi:10.1007/JHEP02(2010)011]. References 33

    2009 doi

  79. [87]

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

    E. Re, “Single-top Wt-channel production matched with parton showers using the POWHEGmethod”,Eur. Phys. J. C71(2011) 1547, doi:10.1140/epjc/s10052-011-1547-z,arXiv:1009.2450

    2011 doi

  80. [88]

    Merging meets matching inMC@NLO

    R. Frederix and S. Frixione, “Merging meets matching inMC@NLO”,JHEP12(2012) 061,doi:10.1007/JHEP12(2012)061,arXiv:1209.6215

    2012 doi

  81. [89]

    Review of particle physics

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

    2024 doi

  82. [90]

    CMSPYTHIA8 colour reconnection tunes based on underlying-event data

    CMS Collaboration, “CMSPYTHIA8 colour reconnection tunes based on underlying-event data”,Eur. Phys. J. C83(2023) 587, doi:10.1140/epjc/s10052-023-11630-8,arXiv:2205.02905

    2023 doi

  83. [91]

    The catchment area of jets

    M. Cacciari, G. P . Salam, and G. Soyez, “The catchment area of jets”,JHEP04(2008) 005,doi:10.1088/1126-6708/2008/04/005,arXiv:0802.1188

    2008 doi

  84. [92]

    Jet algorithms performance in 13 TeV data

    CMS Collaboration, “Jet algorithms performance in 13 TeV data”, CMS Physics Analysis Summary CMS-PAS-JME-16-003, 2017

    2017

  85. [93]

    Performance of the reconstruction and identification of high-momentum muons in proton-proton collisions at √s=13 TeV

    CMS Collaboration, “Performance of the reconstruction and identification of high-momentum muons in proton-proton collisions at √s=13 TeV”,JINST15(2020) P02027,doi:10.1088/1748-0221/15/02/P02027,arXiv:1912.03516

    2020 doi

  86. [94]

    Deep learning in jet reconstruction at CMS

    CMS Collaboration, “Deep learning in jet reconstruction at CMS”, inACAT2017, p. 042029. 2018. J. Phys. Conf. Ser., 1085. doi:10.1088/1742-6596/1085/4/042029

    2018 doi

  87. [95]

    Performance of the DeepJet b tagging algorithm using 41.9 fb −1 of data from proton-proton collisions at 13 TeV with Phase 1 CMS detector

    CMS Collaboration, “Performance of the DeepJet b tagging algorithm using 41.9 fb −1 of data from proton-proton collisions at 13 TeV with Phase 1 CMS detector”, CMS Detector Performance Note CMS-DP-2018-058, 2018

    2018

  88. [96]

    Measurement of differential t t production cross sections using top quarks at large transverse momenta in pp collisions at √s=13 TeV

    CMS Collaboration, “Measurement of differential t t production cross sections using top quarks at large transverse momenta in pp collisions at √s=13 TeV”,Phys. Rev. D103 (2021) 052008,doi:10.1103/PhysRevD.103.052008,arXiv:2008.07860

    2021 doi

  89. [97]

    Automating the construction of jet observables with machine learning

    K. Datta, A. Larkoski, and B. Nachman, “Automating the construction of jet observables with machine learning”,Phys. Rev. D100(2019) 095016, doi:10.1103/PhysRevD.100.095016,arXiv:1902.07180

    2019 doi

  90. [98]

    Novel jet observables from machine learning

    K. Datta and A. Larkoski, “Novel jet observables from machine learning”,JHEP03 (2018) 086,doi:10.1007/JHEP03(2018)086,arXiv:1710.01305

    2018 doi

  91. [99]

    Reports of my demise are greatly exaggerated:N-subjettiness taggers take on jet images

    L. Moore, K. Nordstr ¨om, S. Varma, and M. Fairbairn, “Reports of my demise are greatly exaggerated:N-subjettiness taggers take on jet images”,SciPost Phys.7(2019) 036, doi:10.21468/SciPostPhys.7.3.036,arXiv:1807.04769

    2019 doi

  92. [100]

    Improving neural networks by preventing co-adaptation of feature detectors

    G. E. Hinton et al., “Improving neural networks by preventing co-adaptation of feature detectors”, 2012.arXiv:1207.0580

    2012

  93. [101]

    Rectified linear units improve restricted boltzmann machines

    V . Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines”, inProceedings of the 27th International Conference on Machine Learning. 2010

    2010

  94. [102]

    Rectifier nonlinearities improve neural network acoustic models

    A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models”, inProceedings of the 30th International Conference on Machine Learning. 2013. 34

    2013

  95. [103]

    TUNFOLD, an algorithm for correcting migration effects in high energy physics

    S. Schmitt, “TUNFOLD, an algorithm for correcting migration effects in high energy physics”,JINST7(2012) T10003,doi:10.1088/1748-0221/7/10/T10003, arXiv:1205.6201

    2012 doi

  96. [104]

    Simultaneous determination of several differential observables with statistical correlations

    CMS Collaboration, “Simultaneous determination of several differential observables with statistical correlations”, CMS Note CMS-NOTE-2024-001, 2024

    2024

  97. [105]

    Measurement of the differential t t production cross section as a function of the jet mass and extraction of the top quark mass in hadronic decays of boosted top quarks

    CMS Collaboration, “Measurement of the differential t t production cross section as a function of the jet mass and extraction of the top quark mass in hadronic decays of boosted top quarks”,Eur. Phys. J. C83(2023) 560, doi:10.1140/epjc/s10052-023-11587-8,arXiv:2211.01456

    2023 doi

  98. [106]

    Automated parton-shower variations inPYTHIA8

    S. Mrenna and P . Skands, “Automated parton-shower variations inPYTHIA8”,Phys. Rev. D94(2016) 074005,doi:10.1103/PhysRevD.94.074005, arXiv:1605.08352

    2016 doi

  99. [107]

    PDF4LHC recommendations for LHC Run 2

    J. Butterworth et al., “PDF4LHC recommendations for LHC Run 2”,J. Phys. G43 (2016) 023001,doi:10.1088/0954-3899/43/2/023001,arXiv:1510.03865

    2016 doi

  100. [108]

    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

    2017 doi

  101. [109]

    Investigations of the impact of the parton shower tuning in PYTHIA8 in the modelling of t t at √s=8 and 13 TeV

    CMS Collaboration, “Investigations of the impact of the parton shower tuning in PYTHIA8 in the modelling of t t at √s=8 and 13 TeV”, CMS Physics Analysis Summary CMS-PAS-TOP-16-021, 2016. 35 A Pairwise correlations between observables The pairwise correlations between the obse...

    2016

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