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arXiv · 2105.13761

Detecting Chiral Magnetic Effect via Deep Learning

Yuan-Sheng Zhao·Lingxiao Wang·Kai Zhou·Xu-Guang Huang
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Abstract

The search of chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attentions. Multiple observables have been proposed but all suffer from obstacles due to large background contaminations. In this Letter, we construct an observable-independent CME-meter based on a deep convolutional neural network. After trained over data set 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 a transfer learning manner, the CME-meter is validated in isobaric collision systems, showing good transferability among different colliding systems. Based on variational approaches, we utilize the DeepDream method to derive the most responsive CME-spectra that demonstrates the physical contents the machine learns.

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Yuan-Sheng Zhao, Lingxiao Wang, Kai Zhou, Xu-Guang Huang. 2021-05-28. Detecting Chiral Magnetic Effect via Deep Learning. https://doi.org/10.1103/physrevc.106.l051901

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