Jet tomography in heavy ion collisions with deep learning

TL;DR

This paper introduces a deep learning approach to jet tomography in heavy ion collisions, enabling the analysis of initial jet configurations and their spatial distribution within the quark-gluon plasma, thus advancing the use of jets as tomographic probes.

Contribution

The study presents a novel deep learning method that uncovers the initial configuration and production points of jets, removing biases from final-state interactions in heavy ion collision analysis.

Findings

Enables jet analysis based on initial energy rather than final energy

Allows precise localization of jet production points within the nuclear overlap region

Provides new insights into azimuthal anisotropies related to initial-state effects

Abstract

Deep learning techniques have the power to identify the degree of modification of high energy jets traversing deconfined QCD matter on a jet-by-jet basis. Such knowledge allows us to study jets based on their initial, rather than final energy. We show how this new technique provides unique access to the genuine configuration profile of jets over the transverse plane of the nuclear collision, both with respect to their production point and their orientation. Effectively removing the selection biases induced by final-state interactions, one can in this way analyse the potential azimuthal anisotropies of jet production associated to initial-state effects. Additionally, we demonstrate the capability of our new method to locate with precision the production point of a dijet pair in the nuclear overlap region, in what constitutes an important step forward towards the long term quest of using jets as tomographic probes of the quark-gluon plasma.