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Quark-Gluon tagging performance at the High-Luminosity LHC using constituent-based transformer models

by F. L. Castillo, J. Levêque

Submission summary

Authors (as registered SciPost users): Florencia L. Castillo
Submission information
Preprint Link: https://arxiv.org/abs/2509.14759v1  (pdf)
Date submitted: Sept. 19, 2025, 9:35 a.m.
Submitted by: Florencia L. Castillo
Submitted to: SciPost Physics Proceedings
Proceedings issue: The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025)
Ontological classification
Academic field: Physics
Specialties:
  • High-Energy Physics - Experiment
Approach: Experimental

Abstract

Jet constituents provide a more detailed description of a jet's radiation pattern than global observables. In simulations for ATLAS Run-2 data (2015-2018), transformer-based taggers trained on low-level inputs outperformed traditional methods using high-level variables with conventional neural networks for quark-gluon discrimination. With the upcoming High-Luminosity LHC (HL-LHC), which will deliver higher luminosity and energy, the ATLAS detector will be upgraded with an extended Inner Tracker covering the forward region, previously uncovered by a tracking detector. This work studies how these upgrades will improve the accuracy and robustness of quark-gluon jet taggers.

Current status:
In refereeing

Reports on this Submission

Report #1 by Anonymous (Referee 2) on 2025-11-19 (Contributed Report)

Strengths

1- Paper well written 2- checking the robustness against pileup is very interesting, and the results look promising

Weaknesses

1-It is not always very clear which flavour of ParT is referred to in the text (ParT Const. or ParT Const+tower, etc)

Report

Very interesting study on performance and pileup robustness of quark vs gluon ML jet taggers in HL-LHC conditions. I recommend for publication.

Requested changes

1- consider citing an ATLAS detector or reconstruction paper to introduce "topo-clusters" and "topo-towers" (section 2) 2- section 2 : consider replacing ”these are referred as jet constituents" with "these are referred to as jet constituents" or "these are called jet constituents " 3- section 3 : have the text and plot be more coherent with respect to the tagger type. Section 3.1 mentions "ParT" but the associated figure 1 shows only "ParT Const". 4- section 3.3/ Figure 3: consider adding an explanation for the non-monotonous pileup dependency for low pT jets at high rapidity (performance at 200 PU seems better than at 140) 5- there seems to be an extra space between second author name and the "1" below the title

Recommendation

Publish (meets expectations and criteria for this Journal)

  • validity: -
  • significance: -
  • originality: -
  • clarity: -
  • formatting: -
  • grammar: -

Report #2 by Santiago Folgueras (Referee 1) on 2025-11-18 (Invited Report)

Report

Thanks for a nice manuscript, the paper is well written, the results seem promising. I would like to clarify, however, few points with the authors that I belive it will help them improve the readibility of the paper:

1) I miss a longer description of the taggers, Table 1 provides some description but it might be useful to be more clear on what kind of information enters, for example, for a non-ATLAS expert what is a "topo-tower", aren't PFOs made from topo-towers/clusters and tracks?

2) What is the reason of the ParT discriminator increase in gluon jet rejection shown on Figure 1 (left) around 1.7? Similarly when showing the dependence vs number of interactions (Figure 3), why the PU=200 is better than PU=140 at higher rapidity?

Requested changes

1- include a definition of what a topo-tower is? and why are not incorporated into the PFOs? 2-define what low-pt and high-pt the first time it appears, section 3. 3-Provide a more in-detail information on how the ParT discriminator is built, it is not clear how the extra information from the forward tracker is used.

Recommendation

Ask for minor revision

  • validity: good
  • significance: good
  • originality: good
  • clarity: high
  • formatting: excellent
  • grammar: good

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