[Submitted on 4 Apr 2025] (cross-list from hep-ph)
Optimal Observables for the Chiral Magnetic Effect from Machine Learning
Yuji Hirono🇯🇵 · Kazuki Ikeda🇺🇸 · Dmitri E. Kharzeev🇺🇸 · Ziyi Liu🇨🇳 · Shuzhe Shi🇨🇳
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 parameterizing 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 and 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 CME at RHIC and the LHC.
- Comments:
- 4 pages, 1 figure
- Subjects:
- High Energy Physics — Phenomenology (hep-ph); Nuclear Experiment (nucl-ex); Nuclear Theory (nucl-th)
- arXiv:
- 2504.03248 [pdf]