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

TL;DR

This paper introduces a machine learning framework using an interpretable attention mechanism to identify quenched jets in heavy-ion collisions, effectively handling background noise and detector effects, and setting a new standard for jet tagging.

Contribution

The novel contribution is a model-agnostic, interpretable attention-based method for tagging quenched jets in realistic experimental conditions, improving upon previous approaches.

Findings

Achieved improved accuracy in quenched jet identification.

Effectively handled pileup and background noise.

Set a new benchmark for jet tagging performance.

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.