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Recently, machine learning (ML) techniques have led to a range of numerousdevelopments in the field of nuclear and high-energy physics. In heavy-ioncollisions, the impact parameter of a collision is one of the crucialobservables which has a significant impact on the final state particleproduction. However, calculation of such a quantity is nearly impossible inexperiments as the length scale ranges in the level of a few fermi. In thiswork, 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. Inaddition, we predict an event shape observable, transverse spherocity in Pb-Pbcollisions at $\sqrt{s_{\rm NN}}$ = 2.76 and 5.02 TeV using AMPT and PYTHIA8based on Angantyr model. After a successful implementation in small collisionsystems, the use of transverse spherocity in heavy-ion collisions has potentialto reveal new results from heavy-ion collisions where the production of a QGPmedium is already established. We predict the centrality dependent spherocitydistributions from the training of minimum bias simulated data and it was foundthat the predictions from BDT based ML technique match with true simulateddata. In the absence of experimental measurements, we propose to implementMachine learning based regression technique to obtain transverse spherocityfrom the known final state observables in heavy-ion collisions.

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