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SUMMARY:Deep learning predicted elliptic flow of identified particles in H
 IC at the RHIC and LHC
DTSTART;VALUE=DATE-TIME:20230529T152500Z
DTEND;VALUE=DATE-TIME:20230529T155000Z
DTSTAMP;VALUE=DATE-TIME:20261010T114608Z
UID:indico-contribution-371@indico.ipb.ac.rs
DESCRIPTION:Speakers: Gargely Gabor  Barnaföldi (Wigner Research Centre f
 or Physics\, Hungary)\nRecent developments on a deep learning feed-forward
  network for estimating elliptic flow (v2) coefficients in heavy-ion colli
 sions have shown us the prediction power of this technique. The success of
  the model is mainly the estimation of v2 from final state particle kinema
 tic information and learning the centrality and the transverse momentum (p
 T) dependence of v2. The deep learning model is trained with Pb-Pb collisi
 ons at 5.02 TeV minimum bias events simulated with a multiphase transport 
 model (AMPT). We extend this work to estimate v2 for light-flavor identifi
 ed particles such as π±π±\, K±K±\, and p+pˉp+pˉ in heavy-ion colli
 sions at RHIC and LHC energies. The number of constituent quark (NCQ) scal
 ing is also shown. The evolution of pT-crossing point of v2(pT)\, depictin
 g a change in meson- baryon elliptic flow at intermediate-pT\, is studied 
 for various collision systems and energies. The model is further evaluated
  by training it for different pT regions. These results are compared with 
 the available experimental data wherever possible.\n\nSee: [1] Physical Re
 view D 105\, 114022 (2022)\n     [2] https://arxiv.org/abs/2301.10426"\n\n
 https://events.saifa.rs/event/554/contributions/371/
LOCATION:SANU (Serbian Academy of Science and Arts) - Belgrade\, Serbia
URL:https://events.saifa.rs/event/554/contributions/371/
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