Year: 2022
Type: article
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.
Source: Physical Review C
Institutions: Fudan University, Frankfurt Institute for Advanced Studies
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Awards: G4917834128, 磁场与涡旋场中的夸克胶子物质
OpenAlex ID: W4308026813