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

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 CME dynamics, enabling the reconstruction of the CME 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 CME-induced charge separation based on final-state transverse momentum distributions at either the quark-gloun plasma freeze-out or hadronic freeze-out. This underscores the promise of deep learning approaches in forthcoming experimental searches for the CME and related other physics in HICs.