2025/07/08 by Guo, Shuang, Wang, Lingxiao, Zhou, Kai +1 · 1 citation
#FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #Nuclear Experiment (nucl-ex) #Nuclear Theory (nucl-th)
paper · doi:10.48550/arxiv.2507.05808
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.