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.