Speaker
Description
Using the kinematic information of the final state particles produced in heavy-ion collisions at relativistic energies, one tries to probe the properties of the very hot and dense medium formed just after the collision. There have been different probes to study the physics associated with such a medium, and one of them is the elliptic flow ($v_2$). In this study, we have employed a deep neural network (DNN) based estimator in the machine learning framework to estimate $v_2$, using the particle kinematic information as the input. The DNN model is trained with Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV minimum bias data, simulated with AMPT. The trained model is also evaluated for Pb-Pb collisions at $\sqrt{s_{NN}}$ = 2.76, 5.02 TeV and Au-Au collisions at $\sqrt{s_{NN}}$ = 200 GeV, and is compared with ALICE experimental results. The proposed DNN model preserves the centrality, and transverse momentum dependence of the flow coefficient. It is also found to be quite sturdy when subjected to simulated data with the uncorrelated noise as the prediction accuracy of the DNN model remains intact upto a reasonable extent. Such an estimator is yet to be tested with the experimental inputs along with detector level correlations in future with ALICE.
