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

Shuang Guo (郭爽), Han-Sheng Wang (王瀚生), Kai Zhou (周凯), and Guo-Liang Ma (马国亮)
Phys. Rev. C 110, 024910 – Published 28 August 2024

Abstract

Collective flow has been found to be similar between small colliding systems (p+p and p+A collisions) and large colliding systems (peripheral A+A collisions) at the CERN Large Hadron Collider. In order to study the differences of collective flow between small and large colliding systems, we employ a point-cloud network to identify p+ Pb collisions and peripheral Pb + Pb collisions at sNN=5.02 TeV generated from a multiphase transport model. After removing the discrepancies in the pseudorapidity distribution and the pT spectra, we capture the discrepancy in collective flow. Although the verification accuracy of our PCN is limited due to similar event-by-event distributions of elliptic and triangular flow, we demonstrate that collective flow between p+ Pb collisions and peripheral Pb + Pb collisions becomes more distinct with increasing final hadron multiplicity and parton scattering cross section. This study not only highlights the potential of PCN techniques in advancing the understanding of collective flow in varying colliding systems, but more importantly lays the groundwork for the future PCN-related research.

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  • Received 24 May 2023
  • Revised 5 April 2024
  • Accepted 18 July 2024

DOI:https://doi.org/10.1103/PhysRevC.110.024910

©2024 American Physical Society

Physics Subject Headings (PhySH)

Nuclear PhysicsNetworks

Authors & Affiliations

Shuang Guo (郭爽)1,2, Han-Sheng Wang (王瀚生)1,2, Kai Zhou (周凯)3,*, and Guo-Liang Ma (马国亮)1,2,†

  • 1Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Institute of Modern Physics, Fudan University, Shanghai 200433, China
  • 2Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University, Shanghai 200438, China
  • 3School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong 518172, China

  • *Contact author: zhoukai@cuhk.edu.cn
  • Contact author: glma@fudan.edu.cn

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Issue

Vol. 110, Iss. 2 — August 2024

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