May 16 – 20, 2022
Europe/Zurich timezone

A Deep Learning Based Estimator for Elliptic Flow in Heavy Ion Collisions

May 17, 2022, 7:00 PM
Theory poster Heavy Ions Poster Session I


Mr Neelkamal Mallick (Indian Institute of Technology Indore)


Deep Learning (DL) is one of the most popular Machine Learning models in the High Energy Physics (HEP) community and has been applied to solve numerous problems for decades. The ability of the DL model to learn unique patterns and correlations from data to map highly complex non-linear functions is a matter of interest. Such features of the DL model could be used to explore the hidden physics laws that govern particle production, anisotropic flow, spectra, etc., in heavy-ion collisions. This work sheds light on the possible use of the DL techniques such as the feed-forward Deep Neural Network (DNN) based estimator to predict the elliptic flow ($v_2$) in heavy-ion collisions at RHIC and LHC energies. A novel method is used to process the track level information as input to the DNN model. The model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias simulated events with AMPT event generator. The trained model is successfully applied to estimate centrality dependence of $v_2$ for both LHC and RHIC energies. The proposed model is quite successful in predicting the transverse momentum ($p_{\rm T}$) dependence of $v_2$ as well. A noise sensitivity test is performed to estimate the systematic uncertainty of this method. Results of the DNN estimator are compared to both simulation and experiment, which concludes the robustness and prediction accuracy of the model.

Reference: N. Mallick, S. Prasad, A. N. Mishra, R. Sahoo and G. G. Barnaf\"oldi,
[arXiv:2203.01246 [hep-ph]].

Primary authors

Mr Neelkamal Mallick (Indian Institute of Technology Indore) Suraj Prasad (Indian Institute of Technology Indore (IN)) Aditya Nath Mishra (Wigner Research Centre for Physics Budapest, Hungary) Raghunath Sahoo (Indian Institute of Technology Indore (IN)) Gergely Barnafoldi (Hungarian Academy of Sciences (HU))

Presentation materials