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arXiv · 2507.05808

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

Shuang Guo·Lingxiao Wang·Kai Zhou·Guo-Liang Ma
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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.

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Shuang Guo, Lingxiao Wang, Kai Zhou, Guo-Liang Ma. 2025-10-24. Neural Unfolding of the Chiral Magnetic Effect in Heavy-Ion Collisions. https://doi.org/10.1088/0256-307x%2F42%2F11%2F110101

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