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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 $\sqrt{s_{\rm NN}}
= 5.02$ 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.
References:
(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