A Deep Learning Based Estimator for Light Flavour Elliptic Flow in Heavy Ion Collisions at LHC Energies

Abstract

We developed a deep learning feed-forward network for estimating elliptic flow ($v_2$) coefficients in heavy-ion collisions from RHIC to LHC energies. The success of our model is mainly the estimation of $v_2$ from final state particle kinematic information and learning the centrality and the transverse momentum ($p_{\mathrm{T}}$) dependence of $v_2$ in wide $p_{\mathrm{T}}$ regime. The deep learning model is trained with AMPT-generated Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV minimum bias events. We present $v_2$ estimates for $\ddot{I}{€}^{\pm }$, ${\mathrm{K}}^{\pm }$, and $\mathrm{p}+\bar{\mathrm{p}}$ in heavy-ion collisions at various LHC energies. These results are compared with the available experimental data wherever possible.

4 pages, 2 figures