Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions

TL;DR

This paper introduces a deep learning method using a U-Net architecture to reconstruct the chiral magnetic effect signal in heavy-ion collisions, overcoming background challenges and enabling analysis across the entire collision evolution.

Contribution

It presents a novel deep learning unfolding technique for CME signals in heavy-ion collisions, trained on simulated data, to recover charge separation dynamics from final-state distributions.

Findings

Successfully reconstructs CME signals from simulated data

Demonstrates deep learning's potential in heavy-ion collision analysis

Provides a new tool for studying CME in experimental data

Abstract

The search for the chiral magnetic effect (CME) in relativistic heavy-ion collisions (HICs) is challenged by significant background contamination. We present a novel deep learning approach based on a U-Net architecture to time-reversely unfold the dynamics of CME-related charge separation, enabling the reconstruction of the physics signal across the entire evolution of HICs. Trained on the events simulated by a multi-phase transport model with different cases of CME settings, our model learns to recover the charge separation based on final-state transverse momentum distributions at either the quark-gloun plasma freeze-out or hadronic freeze-out. This devises a methodological tool for the study of CME and underscores the promise of deep learning approaches in retrieving physics signals in HICs.