ELFT HÍRADÓ

[Ferenc Siklér <sikler.ferenc AT wigner.hu>]

Kedves kollégák,

 Szeretettel meghívunk minden érdeklődőt a Wigner FK Nagyenergiás Fizikai
Osztályának jövő hétfői szemináriumára, március 8-án 11:00-ra.

 Részletek és csatlakozási infó [zoom] itt:
 https://indico.kfki.hu/event/1280/

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Neelkamal Mallick (Department of Physics, Indian Institute of Technology 
Indore)

Title: Study of transverse spherocity dependence of particle production and 
application of machine learning techniques in heavy-ion collisions at the LHC

Abstract:
Transverse spherocity has been studied successfully as a tool to separate jetty 
and isotropic events in small collision systems such as proton-proton 
collisions at the LHC. It has a unique capability to distinguish events based 
on their geometrical shapes. For the first time, we have studied the transverse 
spherocity dependence of azimuthal anisotropy in heavy-ion collisions using a 
multi-phase transport model (AMPT). We have followed the two-particle 
correlation method to estimate the elliptic flow (v2) indifferent centrality 
classes in Pb-Pb collisions at 5.02 TeV. A comparative study has been made 
between AMPT and PYTHIA8 (Angantyr) and it is found that the two-particle 
elliptic flow (v2,2) is almost zero in the latter one. We found that transverse 
spherocity successfully differentiates heavy-ion collisions event topology 
based on their geometrical shapes i.e. high and low values of spherocity (S0). 
The high-S0 events are found to have nearly zero elliptic flow while the low-S0 
events contribute significantly to the elliptic flow of spherocity-integrated 
events. Unfortunately, we do not have any experimental exploration in this 
direction at the LHC yet. Thus, we have implemented machine learning-based 
regression techniques to estimate transverse spherocity for heavy-ion 
collisions at the LHC energies. We have presented a unique method where the 
ML-model takes final state observables dNch/dη, NchTS and ⟨pT⟩ as the input 
variables from Pb-Pb collisions at 5.02TeV (min. bias) and estimates S0. We 
have trained the ML-model with min. bias data and then applied it to predict 
the centrality wise S0 distributions. The predictions from ML-model and the 
true values from the simulation are in good agreement with each other. Similar 
studies could be performed in real experiments that could reveal more 
information about the event topology dependence of heavy-ion collisions’ system 
dynamics
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Üdvözlettel,
 Siklér Ferenc