Neelkamal Mallick [1], Suraj Prasad [1], Aditya Nath Mishra [2,4], Raghunath Sahoo [1] and Gergely Gábor Barnaföldi [3] (2023.01.01 - 2023.03.31)

[1] Department of Physics, Indian Institute of Technology Indore
[2] Department of Physics, School of Applied Sciences, REVA University
[3] Wigner Research Center for Physics
[4] Department of Physics, University Centre For Research & Development (UCRD), Chandigarh University

Publication: 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 $(v_2)$ coefficients in heavy-ion collisions have shown the prediction power of this technique. The success of the model is mainly the estimation of $v_2$ from final-state particle kinematic information and learning the centrality and transverse momentum $(p_T)$ dependence of $v_2$. The deep learning model is trained with Pb-Pb collisions at $\sqrt{s_{NN}} = 5.02 TeV$ minimum bias events simulated with a multiphase transport model. We extend this work to estimate $v_2$ for light-flavor identified particles such as $π^\pm$, $K^\pm$, and $p + \bar{p}$ in heavy-ion collisions at RHIC and LHC energies. The number-of-constituent-quark scaling is also shown. The evolution of the $p_T$-crossing point of $v_2(p_T)$, depicting a change in baryon-meson elliptic flow at intermediate $p_T$ , is studied for various collision systems and energies. The model is further evaluated by training it for different $p_T$ regions. These results are compared with the available experimental data wherever possible.