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

TL;DR

This paper introduces a deep learning model that estimates elliptic flow coefficients in heavy-ion collisions using particle kinematic data, effectively capturing dependencies on centrality and transverse momentum across a wide range.

Contribution

It presents a novel deep learning approach trained on AMPT simulations to accurately estimate elliptic flow in heavy-ion collisions at LHC energies.

Findings

Accurately estimates $v_2$ for various particles.

Captures $p_T$ and centrality dependence of $v_2$.

Shows good agreement with experimental data.

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 ($p_{\rm T}$) dependence of $v_2$ in wide $p_{\rm T}$ regime. The deep learning model is trained with AMPT-generated Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events. We present $v_2$ estimates for $\pi^{\pm}$, $\rm K^{\pm}$, and $\rm p+\bar{p}$ in heavy-ion collisions at various LHC energies. These results are compared with the available experimental data wherever possible.