# Deep learning for jet modification in the presence of the QGP background

## TL;DR

This paper demonstrates that graph neural networks applied to particle cloud representations of jets can accurately predict jet energy loss in a quark-gluon plasma environment, outperforming image-based CNNs especially with background noise.

## Contribution

It introduces the use of dynamic graph convolutional neural networks for jet modification prediction in heavy-ion collisions, showing their robustness against background effects.

## Key findings

- DGCNNs maintain high accuracy with background subtraction.
- CNNs perform well without background but degrade with background noise.
- Graph neural networks outperform CNNs in realistic conditions.

## Abstract

Jet interactions with the color-deconfined QCD medium in relativistic heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to their baselines in proton-proton collisions. Deep learning methods enable per-jet evaluation of these modifications, enhancing the use of jets as precision probes of the nuclear medium. In this work, we predict the jet-by-jet fractional energy loss $\chi$ for jets evolving through a quark-gluon plasma (QGP) medium using a Linear Boltzmann Transport (LBT) model. To approximate realistic experimental conditions, we embed medium-modified jets in a thermal background and apply Constituent Subtraction for background removal. Two network architectures are studied: convolutional neural networks (CNNs) using jet images, and dynamic graph convolutional neural networks (DGCNNs) using particle clouds. We find that CNNs achieve accurate predictions for background-free jets but degrade in the presence of the QGP background and remain below the background-free baseline even after background subtraction. In contrast, DGCNNs applied to background-subtracted particle clouds maintain high accuracy across the entire $\chi$ range, demonstrating the advantage of point-cloud-based graph neural networks that exploit full jet structure under realistic conditions.