Jun 7 – 12, 2021
Europe/Paris timezone

Implementation of machine learning techniques to predict impact parameter and transverse spherocity in heavy-ion collisions at the LHC

Jun 10, 2021, 6:45 PM


Theory poster Tools Poster Session


Dr Aditya Nath Mishra (Wigner Research Centre for Physics Budapest, Hungary)


Machine learning techniques have been quite popular recently in the high-energy physics community and have led to numerous developments in this field. In heavy-ion collisions, one of the crucial observables, the impact parameter, plays an important role in the final-state particle production. This being extremely small (i.e. of the order of a few fermi), it is almost impossible to measure impact parameter in experiments. In this work, we implement the ML-based regression technique via Gradient Boosting Decision Trees (GBDT) to obtain a prediction of impact parameter in Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV using A Multi-Phase Transport (AMPT) model. After its successful implementation in small collision systems, transverse spherocity, an event shape observable, holds an opportunity to reveal more about the particle production in heavy-ion collisions as well. In the absence of any experimental exploration in this direction at the LHC yet, we suggest an ML-based regression method to estimate centrality-wise transverse spherocity distributions in Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV by training the model with minimum bias collision data. Throughout this work, we have used a few final state observables as the input to the ML-model, which could be easily made available from collision data. Our method seems to work quite well as we see a good agreement between the simulated true values and the predicted values from the ML-model.

Primary authors

Mr Neelkamal Mallick (Indian Institute of Technology Indore) Sushanta Tripathy (Universita e INFN, Bologna (IT)) Dr Aditya Nath Mishra (Wigner Research Centre for Physics Budapest, Hungary) Suman Deb (Indian Institute of Technology Indore (IN)) Raghunath Sahoo (Indian Institute of Technology Indore (IN))

Presentation materials