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

Abstract

Recent developments on a deep learning feed-forward network for estimating elliptic flow ($v_2$) coefficients in heavy-ion collisions have shown us 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 the transverse momentum ($p_{\mathrm{T}}$) dependence of $v_2$. The deep learning model is trained with Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$ TeV minimum bias events simulated with a multiphase transport model (AMPT). We extend this work to estimate $v_2$ for light-flavor identified particles such as ${\pi }^{\pm }$, ${\mathrm{K}}^{\pm }$, and $\mathrm{p}+\bar{\mathrm{p}}$ in heavy-ion collisions at RHIC and LHC energies. The number of constituent quark (NCQ) scaling is also shown. The evolution of $p_{\mathrm{T}}$-crossing point of $v_2(p_{\mathrm{T}})$, depicting a change in meson-baryon elliptic flow at intermediate-$p_{\mathrm{T}}$, is studied for various collision systems and energies. The model is further evaluated by training it for different $p_{\mathrm{T}}$ regions. These results are compared with the available experimental data wherever possible.

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