Quark-Gluon tagging performance at the High-Luminosity LHC using constituent-based transformer models
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.
Cite
@article{arxiv.2509.14759,
title = {Quark-Gluon tagging performance at the High-Luminosity LHC using constituent-based transformer models},
author = {F. L. Castillo and J Levêque},
journal= {arXiv preprint arXiv:2509.14759},
year = {2025}
}
Comments
Proceedings of the European AI for Fundamental Physics Conference (EuCAIFCon 2025)