Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies

Abstract

Recent developments of a deep learning feed-forward network for estimating elliptic flow (v2) coefficients in heavy-ion collisions have shown the prediction power of this technique. The success of the model is mainly the estimation of v2 from final-state particle kinematic information and learning the centrality and transverse momentum (pT) dependence of v2. The deep learning model is trained with Pb-Pb collisions at sNN=5.02 TeV minimum bias events simulated with a multiphase transport model. We extend this work to estimate v2 for light-flavor identified particles such as π±, K±, and p+p¯ in heavy-ion collisions at RHIC and LHC energies. The number-of-constituent-quark scaling is also shown. The evolution of the pT-crossing point of v2(pT), depicting a change in baryon-meson elliptic flow at intermediate pT, is studied for various collision systems and energies. The model is further evaluated by training it for different pT regions. These results are compared with the available experimental data wherever possible. © 2023 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/"Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Funded by SCOAP3.