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

TL;DR

This paper applies machine learning regression techniques to predict the impact parameter and transverse spherocity in heavy-ion collisions at the LHC, enabling better understanding of collision centrality and event shape without direct measurement.

Contribution

It introduces ML-based regression models, specifically Gradient Boosting Decision Trees, to estimate impact parameter and transverse spherocity in heavy-ion collisions, a novel approach in this context.

Findings

ML models accurately predict impact parameter from collision data.

Transverse spherocity distributions can be estimated using ML, revealing insights into particle production.

Good agreement between predicted and true simulated values demonstrates model effectiveness.

Abstract

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 $\sqrt{s_{NN}}$ = 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 $\sqrt{s_{NN}}$ = 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.