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

TL;DR

This paper applies deep learning to predict elliptic flow coefficients of identified particles in heavy-ion collisions at RHIC and LHC energies, demonstrating the model's ability to learn from simulated data and extend to various particle types and energies.

Contribution

The study extends previous deep learning models to estimate elliptic flow for identified particles and explores the NCQ scaling and $p_T$-crossing points across different collision systems and energies.

Findings

Deep learning accurately predicts $v_2$ for identified particles.

Model reproduces NCQ scaling in elliptic flow.

Results agree with experimental data where available.

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 ($p_{\rm T}$) dependence of $v_2$. The deep learning model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ 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 $\pi^{\pm}$, $\rm K^{\pm}$, and $\rm p+\bar{p}$ in heavy-ion collisions at RHIC and LHC energies. The number of constituent quark (NCQ) scaling is also shown. The evolution of $p_{\rm T}$-crossing point of $v_2(p_{\rm T})$, depicting a change in meson-baryon elliptic flow at intermediate-$p_{\rm T}$, is studied for various collision systems and energies. The model is further evaluated by training it for different $p_{\rm T}$ regions. These results are compared with the available experimental data wherever possible.