Estimating elliptic flow coefficient in heavy ion collisions using deep learning

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