Estimation of impact parameter and transverse spherocity in heavy-ion collisions at the LHC energies using machine learning

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 that has a significant impact on the final state particle production. However, the 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 trees to obtain a prediction of the impact parameter in Pb-Pb collisions at root s(NN) = 5.02 TeV using a multiphase transport model. In addition, we predict an event shape observable, transverse spherocity in Pb-Pb collisions at root s(NN) = 2.76 and 5.02 TeV using a multiphase transport and PYTHIA8 based on Angantyr model. After a successful implementation in small collision systems, the use of transverse spherocity in heavy-ion collisions has potential to reveal new results from heavy-ion collisions where the production of a quark gluon plasma medium is already established. We predict the centrality dependent spherocity distributions from the training of minimum bias simulated data and find that the predictions from the boosted decision trees based ML technique match with true simulated data. In the absence of experimental measurements, we propose to implement a machine learning based regression technique to obtain transverse spherocity from the known final state observables in heavy-ion collisions.