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Detecting the chiral magnetic effect via deep learning

Yuan-Sheng Zhao1,*, Lingxiao Wang2,†, Kai Zhou2,‡, and Xu-Guang Huang1,3,§

  • 1Physics Department and Center for Particle Physics and Field Theory, Fudan University, Shanghai 200433, China
  • 2Frankfurt Institute for Advanced Studies, Ruth Moufang Strasse 1, D-60438, Frankfurt am Main, Germany
  • 3Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University, Shanghai 200433, China

  • *yczhao19@fudan.edu.cn
  • †lwang@fias.uni-frankfurt.de
  • ‡Corresponding author: zhou@fias.uni-frankfurt.de
  • §Corresponding author: huangxuguang@fudan.edu.cn

Extracting transport properties from the heavy-quark potential with neural networks in a holographic model

Wen-Chao Dai, Ou-Yang Luo, Bing Chen, Xun Chen, Xiao-Yan Zhu, and Xiao-Hua Li

Physics Letters B 877 140511 (2026)

Heavy quarkonium spectrum and decay constants from a neural-network-based holographic model

Yu Zhang, Xun Chen, and Miguel Angel Martin Contreras

Phys. Rev. D 113 126029 (2026)

Classification of the flavor dependence of the chiral magnetic effect with a deep neural network using multiple correlators

Somdeep Dey, Abhisek Saha, and Soma Sanyal

Phys. Rev. C 113 024913 (2026)

Deep learning approach for predicting multiple observables in Au+Au collisions at energies available at the BNL Relativistic Heavy Ion Collider

Jun-Qi Tao, Xiang Fan, Yang Liu, Yu Sha, Kai Zhou, Hua Zheng, and Ben-Wei Zhang

Phys. Rev. C 113 064911 (2026)

Optimal observables for the chiral magnetic effect from machine learning

Yuji Hirono, Kazuki Ikeda, Dmitri E. Kharzeev, Ziyi Liu, and Shuzhe Shi

Phys. Rev. C 112 L051904 (2025)

Estimating centrality in heavy-ion collisions using Transfer Learning technique

Dipankar Basak and Kalyan Dey

Nuclear Physics A 1057 123043 (2025)

Constraining the chiral magnetic effect using spectator and participant planes across Au+Au and isobar collisions at $$\sqrt{s_{_\textrm{NN}}} = 200$$ GeV

Bang-Xiang Chen, Xin-Li Zhao, and Guo-Liang Ma

NUCL SCI TECH 36 170 (2025)

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

Shuang 爽 Guo 郭, Lingxiao 凌霄 Wang 王, Kai 凯 Zhou 周, and Guoliang 国亮 Ma 马

Chinese Phys. Lett. 42 110101 (2025)

Exploring QCD matter in extreme conditions with Machine Learning

Kai Zhou, Lingxiao Wang, Long-Gang Pang, and Shuzhe Shi

Progress in Particle and Nuclear Physics 135 104084 (2024)

Machine learning study to identify collective flow in small and large colliding systems

Shuang Guo, Han-Sheng Wang, Kai Zhou, and Guo-Liang Ma

Phys. Rev. C 110 024910 (2024)

Studying high-energy nuclear physics with machine learning

Long-Gang Pang

Int. J. Mod. Phys. E 33 2430009 (2024)

Applying deep learning technique to chiral magnetic wave search*

Yuan-Sheng 渊晟 Zhao 赵 and Xu-Guang 旭光 Huang 黄

Chinese Phys. C 48 084103 (2024)

High-energy nuclear physics meets machine learning

Wan-Bing He, Yu-Gang Ma, Long-Gang Pang, Hui-Chao Song, and Kai Zhou

NUCL SCI TECH 34 88 (2023)

Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning

Dipankar Basak and Kalyan Dey

Eur. Phys. J. A 59 174 (2023)

Identifying lightning structures via machine learning

Lingxiao Wang, Brian M. Hare, Kai Zhou, Horst Stöcker, and Olaf Scholten

Chaos, Solitons & Fractals 170 113346 (2023)

Continuous-Mixture Autoregressive Networks Learning the Kosterlitz-Thouless Transition

Lingxiao Wang, Yin Jiang, Lianyi He, and Kai Zhou

Chinese Phys. Lett. 39 120502 (2022)

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