Estimation of Impact Parameter and Transverse Spherocity in heavy-ion collisions at the LHC energies using Machine Learning

Understand

Recently, machine learning (ML) techniques have led to a range of numerous developments in the field of nuclear and high-energy physics.

• In heavy-ion collisions, the impact parameter of a collision is one of the crucial observables which has a significant impact on the final state particle production.
• However, calculation of such a quantity is nearly impossible in experiments as the length scale ranges in the level of a few fermi.
• In this work, we implement the ML-based regression technique via Boosted Decision Tree (BDT) 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.