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  • Letter
  • Open Access

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
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Phys. Rev. C 106, L051901 – Published 3 November, 2022

DOI: https://doi.org/10.1103/PhysRevC.106.L051901

Abstract

The search for chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attention. Multiple observables are proposed, but all suffer large background contaminations. In this study, we construct an end-to-end CME-meter based on a deep convolutional neural network. After being trained over a dataset generated by a multiphase transport model, the CME-meter shows high accuracy in recognizing the CME-featured charge separation from the final-state pion spectra. It also exhibits remarkable robustness to diverse conditions including different collision energies, centralities, and elliptic flow backgrounds. In extrapolation tests, the CME-meter is validated in isobaric collisions and different model simulation, showing robust generalization performance. Moreover, based on variational approaches, we utilize the DeepDream method to derive the most responsive CME-spectrum that demonstrates the physical contents the machine learned.

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Phys. Rev. C 106, L051901– Published 3 November, 2022
  • Received 10 June 2021
  • Revised 17 August 2022
  • Accepted 18 October 2022
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