Speaker
Description
Recent developments on a deep learning feed-forward network for estimating elliptic flow (v2) coefficients in heavy-ion collisions have shown us the prediction power of this technique. The success of the model is mainly the estimation of v2 from final state particle kinematic information and learning the centrality and the transverse momentum (pT) dependence of v2. The deep learning model is trained with Pb-Pb collisions at 5.02 TeV minimum bias events simulated with a multiphase transport model (AMPT). We extend this work to estimate v2 for light-flavor identified particles such as π±π±, K±K±, and p+pˉp+pˉ in heavy-ion collisions at RHIC and LHC energies. The number of constituent quark (NCQ) scaling is also shown. The evolution of pT-crossing point of v2(pT), depicting 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.
See: [1] Physical Review D 105, 114022 (2022)
[2] https://arxiv.org/abs/2301.10426"
