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  • Letter
  • Open Access

Optimal observables for the chiral magnetic effect from machine learning

Yuji Hirono1,*, Kazuki Ikeda2,3,†, Dmitri E. Kharzeev3,4,‡, Ziyi Liu5,§, and Shuzhe Shi5,6,∥

  • *Contact author: hirono@iit.tsukuba.ac.jp
  • †Contact author: kazuki.ikeda@umb.edu
  • ‡Contact author: dmitri.kharzeev@stonybrook.edu
  • §Contact author: ziyi-liu24@mails.tsinghua.edu.cn
  • ∥Contact author: shuzhe-shi@tsinghua.edu.cn
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Phys. Rev. C 112, L051904 – Published 25 November, 2025

DOI: https://doi.org/10.1103/hxjq-p67d

Abstract

The detection of the chiral magnetic effect (CME) in relativistic heavy-ion collisions remains challenging due to substantial background contributions that obscure the expected signal. In this Letter, we present a novel machine learning approach for constructing optimized observables that significantly enhance CME detection capabilities. By parametrizing generic observables constructed from flow harmonics and optimizing them to maximize the signal-to-background ratio, we systematically develop CME-sensitive measures that outperform conventional methods. Using simulated data from the anomalous viscous fluid dynamics framework, our machine learning observables demonstrate up to 90% higher sensitivity to CME signals compared to traditional γ and δ correlators, while maintaining minimal background contamination. The constructed observables provide physical insight into optimal CME detection strategies and offer a promising path forward for experimental searches of the CME at the BNL Relativistic Heavy Ion Collider and the CERN Large Hadron Collider.

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Phys. Rev. C 112, L051904– Published 25 November, 2025
  • Received 18 May 2025
  • Accepted 23 October 2025
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