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

Umar Sohail Qureshi; Raghav Kunnawalkam Elayavalli

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arXiv · 2411.19389

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

Umar Sohail Qureshi·Raghav Kunnawalkam Elayavalli

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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.

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Umar Sohail Qureshi, Raghav Kunnawalkam Elayavalli. 2024-11-28. Model-Agnostic Tagging of Quenched Jets in Heavy-Ion Collisions. https://arxiv.org/abs/2411.19389

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