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REVIEW 4 major objections 6 minor 61 references

Model-Agnostic Tagging of Quenched Jets in Heavy-Ion Collisions

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A sequential attention network achieves AUC 0.95 for tagging quenched jets under experimentally realistic heavy-ion conditions.

desk verdict Useful realistic-simulation tagger; benchmark comparisons need same-dataset baselines and a leave-one-generator-out test. read the letter →

arxiv 2411.19389 v1 pith:QQYGV5YT submitted 2024-11-28 hep-ph

Umar Sohail Qureshi , Raghav Kunnawalkam Elayavalli This is my paper · ORCID
classification hep-ph
keywords quenchedjettaggingheavy-ioncollisionsquenchingsequentialattentionnetworkmachinelearningpileupquark-gluonplasmasubstructure
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 aims to show that a machine-learning tagger can separate jets that have been quenched by the quark-gluon plasma from vacuum jets even when the data include pileup, an uncorrelated soft thermal background, and detector smearing—conditions that prior tagging studies mostly left out. It trains a sequential attention network on jets from two quenching generators (Jewel and CoLBT-Hydro) against jets from two vacuum generators (Pythia and Herwig), reporting an AUC of 0.95. The same model produces sparse per-jet feature masks that point to which jet observables drive the decision. If these results hold, the framework would give experimental analyses a practical, interpretable tagger and a route from data-driven tagging to physics-informed observable design.

What carries the argument

The carrying object is a sequential attention network of the TabNet type. At each of three decision steps, a sparsemax mask selects a sparse subset of input features; a transformer processes the masked features into a decision vector and a state passed to the next step, while a prior-scale term $\gamma = 1.3$ controls feature reuse. The input representation concatenates global jet observables—multiplicity, inclusive mass, transverse momentum, soft-drop groomed $z_g$, $R_g$, groomed mass, and $k_\perp$—with up to 35 leading constituents ordered by $p_\perp$, each carrying $p_\perp$, $\eta$, $\phi$, and a smeared particle-ID class. The final decision sums ReLU-transformed step outputs, and the normalized aggregate mask (Eq. 8) yields per-jet feature importance. This mechanism is what lets the model both classify and say which features it used.

What would settle it

Retrain the CNN, DeepSets, LSTM, and PFN baselines on the exact mixed-event samples used here and compare AUCs; if the gap to 0.95 largely disappears, the performance claim is not architecture-specific. Separately, train on Jewel/Pythia jets and evaluate on CoLBT-Hydro/Herwig jets: a large drop in AUC would falsify the model-agnostic claim.

Watch

Extended reading notes

Core claim

We introduce an interpretable, sequential attention-based framework that identifies quenched jets in heavy-ion collisions while accounting for pileup, uncorrelated soft background, and detector effects. The model reaches an AUC of 0.95 on mixed events built by embedding dijet events into central PbPb background with 150 pileup interactions and smeared constituents, outperforming published AUC values of 0.67–0.86 from CNNs, DeepSets, LSTM, MLP, and autoencoder-based taggers. By training and evaluating on two quenching models and two vacuum models, we argue the tagger is model-agnostic; by inspecting its sparse feature-selection masks, we show which global and per-constituent features carry the signal. This, we claim, is the first model-agnostic and interpretable quenched-jet tagger under experimentally realistic conditions.

Load-bearing premise

The central claim depends on the assumption that the AUC values quoted from other papers were computed on comparable tasks, so the reported 0.95 is directly better than their 0.67–0.86; the paper does not retrain those baselines on its own samples.

Editorial extensions

If this is right

  • An experimental analysis could deploy the tagger on PbPb data with pileup and detector smearing and expect quenched/vacuum separation at the level reported here.
  • Because the mask is sparse and sample-specific, it can point to the jet constituents and global observables that most distinguish quenched from unquenched jets.
  • Training on two quenching and two vacuum generators is intended to make the tagger robust to modeling assumptions, reducing the model dependence of extraction of quenching signals.
  • The AUC comparison suggests the sequential attention architecture outperforms CNNs, LSTMs, MLPs, and autoencoder-based taggers on this task.
  • The framework could be extended to other jet substructure discrimination problems in heavy-ion collisions.

Reading between the lines

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

  • A direct cross-generator test—training on one quenching model and testing on another—would be needed to confirm that the reported AUC generalizes beyond the specific samples used here; the paper does not report one.
  • Because the baselines in Table II were not retrained on the same dataset, part of the AUC gap may reflect differences in generators, background handling, or detector simulation rather than architecture alone.
  • The per-jet feature masks could be converted into a hand-crafted observable, for instance a weighted combination of the highest- and lowest-$p_\perp$ constituent $p_\perp$ with groomed jet mass, which would let the tagger's signal be reproduced without a network.
  • Applying the same pipeline to experimental pp and PbPb jets, with the network trained on matched simulations, would test whether the 0.95 separation persists after full reconstruction.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper presents a machine learning framework based on a sequential attention mechanism (TabNet) to tag quenched jets in simulated heavy-ion collisions. The training sample combines JEWEL and CoLBT-Hydro jets as signal (label 1) and PYTHIA8 and Herwig7 jets as background (label 0), and the pipeline includes thermal background embedding, pileup, and detector smearing. The authors report an AUC of 0.95, claim that this significantly outperforms previous methods (CNNs, DeepSets, LSTM, PFN, etc.), and advertise the framework as model-agnostic and interpretable via sparse feature-selection masks.

Significance. If the reported performance and generator-agnostic behavior were rigorously established, the framework would be a practically useful tagger for heavy-ion jet substructure studies, and the inclusion of pileup, thermal background, and detector smearing is a step toward experimental realism. The public availability of code and data is a strength. However, the central comparative and model-agnostic claims are currently not supported by the evidence presented: the AUC comparison in Table II mixes results from different simulations and conditions, no baseline is retrained on the authors' dataset, and no cross-generator validation is reported. These gaps are load-bearing for the headline conclusions.

major comments (4)
  1. [Section III, Table II] The performance comparison is not apples-to-apples. The AUC values for the existing methods are taken from papers that use different generators, background treatments, detector simulations, jet definitions, and selection criteria, while the sequential attention model is evaluated on the specific dataset described in Section II A. No baseline (EFN, PFN, LSTM, CNN, MLP, etc.) is retrained on the same embedded, smeared samples with the same train/test split. Therefore the claim that the sequential attention framework 'significantly outperforms' prior methods is not established. The authors should retrain at least one or two representative baseline models on their own data and report the resulting AUCs under identical conditions.
  2. [Section II B (train/test split)] The 90-10 train/test split is described only at the level of jets, not events. Because the samples are generated by embedding hard-scattering dijet events into thermal background and pileup events, jets from the same underlying event can appear in both the training and test sets. The shared soft background, pileup composition, and detector smearing realizations can then leak information and inflate the reported AUC. The paper should specify whether an event-level split was used, and if not, it should be performed and the AUC recomputed. Reporting per-generator AUCs (e.g., JEWEL vs PYTHIA, JEWEL vs HERWIG, CoLBT-Hydro vs PYTHIA, CoLBT-Hydro vs HERWIG) would also clarify what the model actually separates.
  3. [Section II A / Section IV (model-agnostic claim)] The paper labels JEWEL and CoLBT-Hydro as signal and PYTHIA and Herwig as background, but it never tests whether the trained model transfers across generators. Since the input includes global soft-drop observables (zg, Rg, kT, mg), particle multiplicities, and PID information, a classifier could achieve a high AUC by separating the four generators according to hadronization or shower-model differences rather than by learning medium-induced quenching. A leave-one-generator-out test (for example, train on JEWEL vs PYTHIA and evaluate on CoLBT-Hydro vs HERWIG, plus the reverse) is necessary to support the claim of 'reduced model dependence' and the descriptor 'model-agnostic.' Without such a test, the central physics interpretation of the 0.95 AUC is not established.
  4. [Section III, Fig. 4 and surrounding text] The interpretability claim is supported only by qualitative heatmaps for one JEWEL jet and one PYTHIA jet. There is no quantitative analysis of the aggregate feature masks over many jets, no stability check across generators, and no numerical demonstration that 'features corresponding to the highest and lowest pT jet constituents play the biggest roles.' Since interpretability is advertised as a unique advantage, this analysis should either be made quantitative (e.g., distributions of mask values, feature-ranking statistics, uncertainty estimates) or the claim should be softened accordingly.
minor comments (6)
  1. [Section II A] The phrase 'with bp⊥ > 100 GeV' appears to contain a typographical or notation error; presumably the hard-scattering transverse-momentum cutoff is meant, but it should be written unambiguously (e.g., p̂T or pT,hard).
  2. [Section II A, particle ID encoding] Encoding particle types as values 0, 0.1, 0.2, ... imposes an arbitrary ordinal relationship on categorical labels. A one-hot or learned embedding would avoid this unnecessary inductive bias.
  3. [Equation (1)] The notation in Eq. (1) is ambiguous: the global observables and the per-constituent list are not clearly separated, and the index 'nM i=1' is missing a product or set symbol. Please rewrite to make the concatenation explicit.
  4. [Section III, ROC curve] The ROC curve in Fig. 2 reports a single AUC value without an uncertainty estimate. Given the sample size, a bootstrap confidence interval would make the result more informative and comparable.
  5. [Section III, Fig. 4] The heatmaps lack a color scale, and the x-axis feature index is not mapped to specific features; adding a legend would allow the reader to interpret the sparsity claims.
  6. [Section IV] The sentence 'This not only aids in improving the classification performance but also opens new avenues...' appears to be missing a clause after 'while irrelevant information'; please revise for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the tagger is a supervised classifier evaluated on held-out simulated jets, with labels assigned by generator identity and no prediction reducing to the model's own output.

full rationale

The paper's central claim is a machine-learning classification result: a sequential attention model trained to separate jets from quenching generators (JEWEL, CoLBT-Hydro) from vacuum generators (Pythia, Herwig), evaluated on a held-out 10% split. The truth labels are assigned from generator identity, not from the model output, and the reported AUC measures generalization to unseen jets from the same generator mixture. This is a standard supervised-learning setup and does not reduce to a fitted parameter renamed as a prediction. The attention masks are post-hoc explanations of the trained model, not independent physics predictions. The comparison in Table II quotes published AUC values from other papers; those values are not fitted or derived within this work, so any concern about comparability is a benchmarking-validity issue rather than circularity. Self-citations appear (e.g., the Herwig Nashville tune, Ref. [42], and a prior TabNet application, Ref. [56]), but they are not load-bearing: the paper explicitly states that the Nashville and default tunes give no significant differences, and the sequential-attention architecture is independently attributable to Ref. [50]. The broader scientific concern that the classifier might be learning generator-specific features rather than universal quenching physics is a real external-validity risk, but it is not a circularity of the derivation chain under the rubric used here.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim depends on the realism of the simulated samples and on the comparability of prior AUC values, not on new physics entities or parameters. The free parameters are ML hyperparameters and detector treatment choices.

free parameters (3)
  • TabNet hyperparameters = nd=na=8, Nsteps=3, gamma=1.3, learning_rate=0.02, batch_size=16384, early_stopping patience=10
    Chosen by hand and affect the reported AUC; no hyperparameter sensitivity scan is shown.
  • Detector smearing parameters = 10% pT resolution, angular resolution 0.12, pT cut 0.5 GeV
    Ad hoc choices defining what is called 'detector effects'; the paper notes they are simplified, but the claim of realistic conditions depends on them.
  • Input features and constituent truncation = 35 constituents, 9 global jet variables
    Fixed-length jet representation; the cutoff at 35 particles is arbitrary and not tested for sensitivity.
assumptions (4)
  • domain assumption Jewel and CoLBT-Hydro provide adequate descriptions of jet quenching in QGP.
    The quenched class label is defined by these generators; if the generators do not represent real quenching, the tagger learns simulation-specific differences. Section II A.
  • domain assumption Pythia and Herwig represent unquenched (vacuum) jets, and their differences are not relevant to the classification.
    Both vacuum generators are used as negatives; any generator-specific artifact is treated as part of the signal. Section II A.
  • domain assumption The Boltzmann-type thermal background and 150 pileup interactions faithfully mimic central PbPb conditions.
    The background parameters come from ALICE data (dN/deta), but the superposition and pileup model are assumptions. Section II A and Table I.
  • standard math Soft drop, anti-kT, and FastJet algorithms are standard and correctly implemented.
    Used for jet clustering and grooming without independent verification in this paper. Section II A.

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Cite this review

Pith. "Pith review of Model-Agnostic Tagging of Quenched Jets in Heavy-Ion Collisions." pith.science (2026). https://pith.science/paper/QQYGV5YT

@misc{pith2026241119389,
  author       = {Pith},
  title        = {Pith review of: Model-Agnostic Tagging of Quenched Jets in Heavy-Ion Collisions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QQYGV5YT}},
  note         = {Machine review of arXiv:2411.19389}
}
read the original abstract

Measurements of jet substructure in ultra-relativistic heavy-ion collisions indicate that interactions with the quark-gluon plasma quench the jet showering process. Modern data-driven methods have shown promise in probing these modifications in the jet's hard substructure. In this Letter, we present a machine learning framework to identify quenched jets while accounting for pileup, uncorrelated soft particle background, and detector effects; a more experimentally realistic and challenging scenario than previously addressed. Our approach leverages an interpretable sequential attention-based mechanism that integrates representations of individual jet constituents alongside global jet observables as features. The framework sets a new benchmark for tagging quenched jets with reduced model dependence.

Figures

Figures reproduced from arXiv: 2411.19389 by the authors.

Figure 1
Figure 1. shows the model output distributions for jets simulated using Jewel, CoLBT-Hydro, Pythia, and Herwig. The distributions reveal distinct, order-of￾magnitude separation between quenched and vacuum jets, highlighting the model’s ability to differentiate be￾tween them [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. ROC curve for the separation of the vacuum and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Distributions of the inclusive mass [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Heatmap illustrations of the aggregate feature mask (Eq. 8) for the first 125 [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

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Reference graph

Works this paper leans on

61 extracted references · 6 canonical work pages

  1. [1]

    Mehtar-Tani, J

    Y. Mehtar-Tani, J. G. Milhano, and K. Tywoniuk, Jet physics in heavy-ion collisions, Int. J. Mod. Phys. A 28, 1340013 (2013), arXiv:1302.2579 [hep-ph]

    arXiv 2013

  2. [2]

    J. D. Bjorken, Energy loss of energetic partons in quark-gluon plasma: possible extinction of high pT jets in hadron-hadron collisions, Tech. Rep. (FERMILAB, Batavia, IL, 1982)

    work page 1982

  3. [3]

    d’Enterria, Jet quenching, Landolt-Bornstein 23, 471 (2010), arXiv:0902.2011 [nucl-ex]

    D. d’Enterria, Jet quenching, Landolt-Bornstein 23, 471 (2010), arXiv:0902.2011 [nucl-ex]

    arXiv 2010

  4. [4]

    Majumder and M

    A. Majumder and M. Van Leeuwen, The Theory and Phenomenology of Perturbative QCD Based Jet Quenching, Prog. Part. Nucl. Phys. 66, 41 (2011), arXiv:1002.2206 [hep-ph]

    arXiv 2011

  5. [5]

    Qin and X.-N

    G.-Y. Qin and X.-N. Wang, Jet quenching in high-energy heavy-ion collisions, Int. J. Mod. Phys. E 24, 1530014 (2015), arXiv:1511.00790 [hep-ph]

    arXiv 2015

  6. [6]

    Adcox et al

    K. Adcox et al. (PHENIX), Suppression of hadrons with large transverse momentum in central Au+Au collisions at √sN N= 130-GeV, Phys. Rev. Lett.88, 022301 (2002), arXiv:nucl-ex/0109003

    arXiv 2002

  7. [7]

    Adler et al

    C. Adler et al. (STAR), Centrality dependence of high pT hadron suppression in Au+Au collisions at √sN N= 130-GeV, Phys. Rev. Lett.89, 202301 (2002), arXiv:nucl- ex/0206011

    work page arXiv 2002

  8. [8]

    Aamodt et al.(ALICE), Suppression of Charged Par- ticle Production at Large Transverse Momentum in Cen- tral Pb-Pb Collisions at √sN N= 2.76 TeV, Phys

    K. Aamodt et al.(ALICE), Suppression of Charged Par- ticle Production at Large Transverse Momentum in Cen- tral Pb-Pb Collisions at √sN N= 2.76 TeV, Phys. Lett. B 696, 30 (2011), arXiv:1012.1004 [nucl-ex]

    arXiv 2011

Show all 61 references
  1. [9]

    S. S. Adler et al.(PHENIX), Suppressed π0 production at large transverse momentum in central Au+ Au collisions at √SN N= 200 GeV, Phys. Rev. Lett.91, 072301 (2003), arXiv:nucl-ex/0304022

    2003 arXiv

  2. [10]

    Adams et al.(STAR), Transverse momentum and colli- sion energy dependence of high p(T) hadron suppression in Au+Au collisions at ultrarelativistic energies, Phys

    J. Adams et al.(STAR), Transverse momentum and colli- sion energy dependence of high p(T) hadron suppression in Au+Au collisions at ultrarelativistic energies, Phys. Rev. Lett. 91, 172302 (2003), arXiv:nucl-ex/0305015

    2003 arXiv

  3. [11]

    Adler et al

    C. Adler et al. (STAR), Disappearance of back-to-back high pT hadron correlations in central Au+Au collisions at √sN N= 200-GeV, Phys. Rev. Lett.90, 082302 (2003), arXiv:nucl-ex/0210033

    2003 arXiv

  4. [12]

    Aamodt et al.(ALICE), Particle-yield modification in jet-like azimuthal di-hadron correlations in Pb-Pb colli- sions at √sN N= 2.76 TeV, Phys

    K. Aamodt et al.(ALICE), Particle-yield modification in jet-like azimuthal di-hadron correlations in Pb-Pb colli- sions at √sN N= 2.76 TeV, Phys. Rev. Lett. 108, 092301 (2012), arXiv:1110.0121 [nucl-ex]

    2012 arXiv

  5. [13]

    Chatrchyan et al

    S. Chatrchyan et al. (CMS), Study of High-pT Charged Particle Suppression in PbPb Compared to pp Collisions at √sN N= 2.76 TeV, Eur. Phys. J. C 72, 1945 (2012), arXiv:1202.2554 [nucl-ex]

    2012 arXiv

  6. [14]

    Aad et al.(ATLAS), Measurement of charged-particle spectra in Pb+Pb collisions at √sNN = 2 .76 TeV with the ATLAS detector at the LHC, JHEP 09, 050, arXiv:1504.04337 [hep-ex]

    G. Aad et al.(ATLAS), Measurement of charged-particle spectra in Pb+Pb collisions at √sNN = 2 .76 TeV with the ATLAS detector at the LHC, JHEP 09, 050, arXiv:1504.04337 [hep-ex]

    arXiv

  7. [15]

    Khachatryan et al

    V. Khachatryan et al. (CMS), Charged-particle nuclear modification factors in PbPb and pPb collisions at√sN N = 5 .02 TeV, JHEP 04, 039, arXiv:1611.01664 [nucl-ex]

    arXiv

  8. [16]

    Aad et al

    G. Aad et al. (ATLAS), Observation of a Centrality- Dependent Dijet Asymmetry in Lead-Lead Collisions at√sN N= 2.77 TeV with the ATLAS Detector at the LHC, Phys. Rev. Lett. 105, 252303 (2010), arXiv:1011.6182 [hep-ex]

    2010 arXiv

  9. [17]

    Chatrchyan et al

    S. Chatrchyan et al. (CMS), Observation and studies of jet quenching in PbPb collisions at nucleon-nucleon center-of-mass energy = 2.76 TeV, Phys. Rev. C 84, 024906 (2011), arXiv:1102.1957 [nucl-ex]

    2011 arXiv

  10. [18]

    Chatrchyan et al.(CMS), Jet Momentum Dependence of Jet Quenching in PbPb Collisions at √sN N = 2 .76 TeV, Phys

    S. Chatrchyan et al.(CMS), Jet Momentum Dependence of Jet Quenching in PbPb Collisions at √sN N = 2 .76 TeV, Phys. Lett. B 712, 176 (2012), arXiv:1202.5022 [nucl-ex]

    2012 arXiv

  11. [19]

    Khachatryan et al

    V. Khachatryan et al. (CMS), Measurement of trans- verse momentum relative to dijet systems in PbPb and pp collisions at √sNN = 2 .76 TeV, JHEP 01, 006, arXiv:1509.09029 [nucl-ex]

    arXiv

  12. [20]

    A. M. Sirunyan et al. (CMS), Measurement of the Splitting Function in pp and Pb-Pb Collisions at√sNN = 5.02 TeV, Phys. Rev. Lett. 120, 142302 (2018), arXiv:1708.09429 [nucl-ex]

    2018 arXiv

  13. [21]

    M. S. Abdallah et al.(STAR), Differential measurements of jet substructure and partonic energy loss in Au+Au collisions at √SN N=200 GeV, Phys. Rev. C 105, 044906 (2022), arXiv:2109.09793 [nucl-ex]

    2022

  14. [22]

    S. Acharya et al.(A Large Ion Collider Experiment, AL- ICE), Measurement of the groomed jet radius and mo- mentum splitting fraction in pp and Pb −Pb collisions at√sN N= 5.02 TeV, Phys. Rev. Lett. 128, 102001 (2022), arXiv:2107.12984 [nucl-ex]

    2022 arXiv

  15. [23]

    Chatrchyan et al

    S. Chatrchyan et al. (CMS), Modification of Jet Shapes in PbPb Collisions at √sN N= 2.76 TeV, Phys. Lett. B 730, 243 (2014), arXiv:1310.0878 [nucl-ex]

    2014 arXiv

  16. [24]

    Acharya et al

    S. Acharya et al. (ALICE), Measurement of jet radial profiles in Pb–Pb collisions at √sNN = 2.76 TeV, Phys. Lett. B 796, 204 (2019), arXiv:1904.13118 [nucl-ex]

    2019 arXiv

  17. [25]

    Aad et al

    G. Aad et al. (ATLAS), Measurement of angular and momentum distributions of charged particles within and around jets in Pb+Pb and pp collisions at √sNN = 5.02 TeV with the ATLAS detector, Phys. Rev. C100, 064901 (2019), [Erratum: Phys.Rev.C 101, 059903 (2020)], arXiv:1908.0526...

    2019 arXiv

  18. [26]

    Chatrchyan et al

    S. Chatrchyan et al. (CMS), Measurement of Jet Frag- mentation in PbPb and pp Collisions at √sN N= 2 .76 TeV, Phys. Rev. C 90, 024908 (2014), arXiv:1406.0932 [nucl-ex]

    2014 arXiv

  19. [27]

    Aaboud et al.(ATLAS), Measurement of jet fragmen- tation in Pb+Pb and pp collisions at √sNN = 2.76 TeV with the ATLAS detector at the LHC, Eur

    M. Aaboud et al.(ATLAS), Measurement of jet fragmen- tation in Pb+Pb and pp collisions at √sNN = 2.76 TeV with the ATLAS detector at the LHC, Eur. Phys. J. C 77, 379 (2017), arXiv:1702.00674 [hep-ex]

    2017 arXiv

  20. [28]

    Chen, K.-M

    S.-Y. Chen, K.-M. Shen, X.-F. Xue, W. Dai, B.-W. Zhang, and E.-K. Wang, Study of eec discrimination power on quark and gluon quenching effects in heavy- ion collisions at √s = 5.02 tev (2024), arXiv:2409.13996 [nucl-th]

    2024 arXiv

  21. [29]

    HEP ML Community, A Living Review of Machine Learning for Particle Physics

  22. [30]

    L. Liu, J. Velkovska, Y. Wu, and M. Verweij, Identify- ing quenched jets in heavy ion collisions with machine learning, JHEP 04, 140, arXiv:2206.01628 [hep-ph]

    arXiv

  23. [31]

    Apolin´ ario, N

    L. Apolin´ ario, N. F. Castro, M. Crispim Rom˜ ao, J. G. Milhano, R. Pedro, and F. C. R. Peres, Deep Learn- ing for the classification of quenched jets, JHEP 11, 219, arXiv:2106.08869 [hep-ph]

    arXiv

  24. [32]

    Y. S. Lai, J. Mulligan, M. P losko´ n, and F. Ringer, The information content of jet quenching and ma- 7 chine learning assisted observable design, JHEP 10, 011, arXiv:2111.14589 [hep-ph]

    arXiv

  25. [33]

    Crispim Rom˜ ao, J

    M. Crispim Rom˜ ao, J. G. Milhano, and M. van Leeuwen, Jet substructure observables for jet quenching in quark gluon plasma: A machine learning driven analysis, Sci- Post Phys. 16, 015 (2024), arXiv:2304.07196 [hep-ph]

    2024 arXiv

  26. [34]

    Y.-L. Du, Overview: Jet quenching with machine learn- ing, in 11th International Conference on Hard and Elec- tromagnetic Probes of High-Energy Nuclear Collisions: Hard Probes 2023(2023) arXiv:2308.10035 [hep-ph]

    2023 arXiv

  27. [35]

    This multi-model approach allows our framework to generalize across different theoretical descriptions of jet quenching

    and CoLBT-Hydro [36], alongside non-quenching models, Pythia8 [37, 38] and Herwig7 [39, 40]. This multi-model approach allows our framework to generalize across different theoretical descriptions of jet quenching. Our method achieves a classification area under the curve (AUC)...

    2024 arXiv

  28. [36]

    K. C. Zapp, F. Krauss, and U. A. Wiedemann, A per- turbative framework for jet quenching, JHEP 03, 080, arXiv:1212.1599 [hep-ph]

    arXiv

  29. [37]

    W. Chen, S. Cao, T. Luo, L.-G. Pang, and X.-N. Wang, Effects of jet-induced medium excitation in γ-hadron cor- relation in A+A collisions, Phys. Lett. B 777, 86 (2018), arXiv:1704.03648 [nucl-th]

    2018 arXiv

  30. [38]

    Sj¨ ostrand, S

    T. Sj¨ ostrand, S. Ask, J. R. Christiansen, R. Corke, N. De- sai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen, and P. Z. Skands, An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191, 159 (2015), arXiv:1410.3012 [hep- ph]

    2015 arXiv

  31. [39]

    Sjostrand, S

    T. Sjostrand, S. Mrenna, and P. Z. Skands, A Brief Intro- duction to PYTHIA 8.1, Comput. Phys. Commun. 178, 852 (2008), arXiv:0710.3820 [hep-ph]

    2008 arXiv

  32. [40]

    Bewick, S

    G. Bewick, S. F. Ravasio, S. Gieseke, S. Kiebacher, M. R. Masouminia, A. Papaefstathiou, S. Pl¨ atzer, P. Richardson, D. Samitz, M. H. Seymour, A. Si´ odmok, and J. Whitehead, Herwig 7.3 release note (2024), arXiv:2312.05175 [hep-ph]

    2024 arXiv

  33. [41]

    Bahr et al., Herwig++ Physics and Manual, Eur

    M. Bahr et al., Herwig++ Physics and Manual, Eur. Phys. J. C 58, 639 (2008), arXiv:0803.0883 [hep-ph]

    2008 arXiv

  34. [42]

    Skands, S

    P. Skands, S. Carrazza, and J. Rojo, Tuning PYTHIA 8.1: the Monash 2013 Tune, Eur. Phys. J. C 74, 3024 (2014), arXiv:1404.5630 [hep-ph]

    2014 arXiv

  35. [43]

    U. S. Qureshi, R. K. Elayavalli, L. Mozarsky, H. Caines, and I. Mooney, A new herwig7 underlying event tune: from rhic to lhc energies (2024), arXiv:2411.16897 [hep- ph]

    2024

  36. [44]

    H. A. Andrews et al., Novel tools and observables for jet physics in heavy-ion collisions, J. Phys. G 47, 065102 (2020), arXiv:1808.03689 [hep-ph]

    2020 arXiv

  37. [45]

    Adam et al

    J. Adam et al. (ALICE), Centrality dependence of the pseudorapidity density distribution for charged particles in Pb-Pb collisions at √sNN = 5.02 TeV, Phys. Lett. B 772, 567 (2017), arXiv:1612.08966 [nucl-ex]

    2017 arXiv

  38. [46]

    Berta, M

    P. Berta, M. Spousta, D. W. Miller, and R. Leitner, Particle-level pileup subtraction for jets and jet shapes, JHEP 06, 092, arXiv:1403.3108 [hep-ex]

    arXiv

  39. [47]

    Cacciari, G

    M. Cacciari, G. P. Salam, and G. Soyez, The anti- kt jet clustering algorithm, JHEP 04, 063, arXiv:0802.1189 [hep-ph]

    arXiv

  40. [48]

    Cacciari, G

    M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual, Eur. Phys. J. C 72, 1896 (2012), arXiv:1111.6097 [hep-ph]

    2012 arXiv

  41. [49]

    Cacciari and G

    M. Cacciari and G. P. Salam, Dispelling the N 3 myth for the kt jet-finder, Phys. Lett. B 641, 57 (2006), arXiv:hep- ph/0512210

    2006

  42. [50]

    A. J. Larkoski, S. Marzani, G. Soyez, and J. Thaler, Soft Drop, JHEP 05, 146, arXiv:1402.2657 [hep-ph]

    arXiv

  43. [51]

    S. O. Arik and T. Pfister, Tabnet: Attentive interpretable tabular learning (2020), arXiv:1908.07442 [cs.LG]

    2020 arXiv

  44. [52]

    X. Ai, S. C. Hsu, K. Li, and C. T. Lu, Probing highly collimated photon-jets with deep learning, JPCS 2438, 012114 (2023)

    2023

  45. [53]

    A. Collaboration (ATLAS), Search for the standard model Higgs boson produced in association with top quarks and decaying into a b¯b pair in pp collisions at√s = 13 TeV with the ATLAS detector, Phys. Rev. D 97, 072016 (2018), arXiv:1712.08895 [hep-ex]

    2018 arXiv

  46. [54]

    Arganda, D

    E. Arganda, D. A. D ´ ıaz, A. D. Perez, R. M. Sand´ a Seoane, and A. Szynkman, Lhc study of third- generation scalar leptoquarks with machine-learned like- lihoods, Phys. Rev. D 109, 055032 (2024)

    2024

  47. [55]

    Fl´ orez, A

    A. Fl´ orez, A. Gurrola, C. Rodriguez, and U. S. Qureshi, Probing light scalars and vector-like quarks at the high- luminosity lhc (2024), arXiv:2410.17854 [hep-ph]

    2024 arXiv

  48. [56]

    Whiteson, HIGGS, UCI Machine Learning Repository (2014), DOI: https://doi.org/10.24432/C5V312

    D. Whiteson, HIGGS, UCI Machine Learning Repository (2014), DOI: https://doi.org/10.24432/C5V312

    2014 doi

  49. [57]

    U. S. Qureshi, A. Gurrola, and A. Fl´ orez, Prob- ing compressed mass spectrum supersymmetry at the lhc with the vector boson fusion topology (2024), arXiv:2411.13837 [hep-ph]

    2024

  50. [58]

    Mikuni and F

    V. Mikuni and F. Canelli, Abcnet: an attention- based method for particle tagging, The European Phys- ical Journal Plus 135, 10.1140/epjp/s13360-020-00497-3 (2020)

    2020 doi

  51. [59]

    Paszke, S

    A. Paszke, S. Gross, F. Massa, A. Lerer, J. Brad- bury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. K¨ opf, E. Yang, Z. De- Vito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, Pytorch: An impera- tive style, ...

    2019 arXiv

  52. [60]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cour- napeau, M. Brucher, M. Perrot, and E. Duchesnay, Scikit-learn: Machine learning in Python, Journal of Ma- chine Lear...

    2011

  53. [61]

    D. P. Kingma and J. Ba, Adam: A method for stochastic optimization (2017), arXiv:1412.6980 [cs.LG]

    2017 arXiv

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.