Előadó: Neelkamal Mallick (India)

Az előadás témája: Event shape dependence of azimuthal anisotropy and application of machine learning tools in heavy-ion collisions at the LHC using a multi-phase transport model

Az előadás időpontja: 2021. október 22., 14 óra

Helyszín: 3-as épület, Tanácsterem


Recently, event shape observables such as transverse spherocity, has been studied successfully in small collision systems at the LHC as a tool to separate jetty and isotropic events. In this work, we have performed an extensive study of charged particles' azimuthal anisotropy in heavy-ion collisions as a function of spherocity for the first time using a multi-phase transport (AMPT) model. We have used the two-particle correlation (2PC) method to estimate the elliptic flow for different centrality classes in Pb-Pb collisions at sNN−−−√=5.02 TeV for high spherocity, spherocity-integrated and low spherocity events. It is found that transverse spherocity successfully differentiates heavy-ion collisions’ event topology based on their geometrical shapes i.e. high and low values of spherocity. The high-spherocity events are found to have nearly zero elliptic flow while the low spherocity events contribute significantly to elliptic flow of spherocity-integrated events. In the absence of experimental explorations in this direction, we have implemented the ML-based regression technique via Gradient Boosting Decision Trees (GBDTs) to estimate spherocity distributions in Pb-Pb collisions at 5.02 TeV c.m. energy by training the model with experimentally available event properties. This ML-model also estimates the impact parameter in heavy-ion collisions. Throughout this work, we have used 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.

    (1)  Neelkamal Mallick, Raghunath Sahoo, Sushanta Tripathy, and Antonio Ortiz, J.Phys.G 48 (2021) 4, 045104

    (2)  Neelkamal Mallick, Sushanta Tripathy, Aditya Nath Mishra, Suman Deb, and Raghunath Sahoo, Phys.Rev.D 103 (2021) 9, 094031