Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning

TL;DR

This paper develops a deep learning approach to estimate the elliptic flow coefficient in heavy-ion collisions, demonstrating its effectiveness and robustness across different energies and conditions.

Contribution

A novel deep neural network method for estimating elliptic flow in heavy-ion collisions, incorporating particle kinematic information and validated against simulation and experimental data.

Findings

The DNN preserves centrality and energy dependence of v2.

The model accurately predicts pT dependence of v2.

Robustness maintained under noisy event simulations.

Abstract

Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate elliptic flow ($v_2$) in heavy-ion collisions at the RHIC and LHC energies. A novel method is developed to process the input observables from particle kinematic information. The proposed DNN model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with AMPT model. The predictions from the ML technique are compared to both simulation and experiment. The Deep Learning model seems to preserve the centrality and energy dependence of $v_2$ for the LHC and RHIC energies. The DNN model is also quite successful in predicting the $p_{\rm T}$ dependence of $v_2$. When subjected to event simulation with additional noise, the proposed DNN model still keeps the robustness and prediction accuracy intact up to a reasonable extent.