https://doi.org/10.1051/epjconf/202531603004
A Deep Learning Based Estimator for Light Flavour Elliptic Flow in Heavy Ion Collisions at LHC Energies
1 HUN-REN Wigner Research Center for Physics, P.O. Box 49, 1125 Budapest, Hungary
2 Department of Physics, Indian Institute of Technology Indore, Simrol, Indore 453552, India
3 University of Jyväskylä, Department of Physics, P.O. Box 35, FI-40014, Jyväskylä, Finland
4 University Centre of Research and Development Department, Chandigarh University, Gharuan, Mohali - 140413, Punjab, India
* e-mail: Barnafoldi.Gergely@wigner.hun-ren.hu
Published online: 27 January 2025
We developed a deep learning feed-forward network for estimating elliptic flow (v2) coefficients in heavy-ion collisions from RHIC to LHC energies. The success of our model is mainly the estimation of v2 from final state particle kinematic information and learning the centrality and the transverse momentum (pT) dependence of v2 in wide pT regime. The deep learning model is trained with AMPT-generated Pb-Pb collisions at √sNN = 5.02 TeV minimum bias events. We present v2 estimates for π±, K±, and p + p¯ in heavy-ion collisions at various LHC energies. These results are compared with the available experimental data wherever possible.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
