A Deep Learning Based Estimator for Light Flavour Elliptic Flow in Heavy Ion Collisions at LHC Energies
Barnaföldi, G. G., Mallick, N., Prasad, S., Sahoo, R., & Mishra, A. N. (2025). A Deep Learning Based Estimator for Light Flavour Elliptic Flow in Heavy Ion Collisions at LHC Energies. In C. Cheshkov, R. Guernane, & A. Maire (Eds.), The 21st International Conference on Strangeness in Quark Matter (SQM 2024) (Article 03004). EDP Sciences. EPJ Web of Conferences, 316 . https://doi.org/10.1051/epjconf/202531603004
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We developed a deep learning feed-forward network for estimating elliptic flow (v2) coefficients in heavy-ion collisions from RHIC to LHC energies. The success of our 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 in wide pT regime. The deep learning model is trained with AMPT-generated Pb-Pb collisions at √sNN = 5.02 TeV minimum bias events. We present v2 estimates for π±, K±, and p + p in heavy-ion colli- ¯ sions at various LHC energies. These results are compared with the available experimental data wherever possible.
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https://converis.jyu.fi/converis/portal/detail/Publication/245389301
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SP acknowledges the doctoral fellowship from UGC, Govt. of India. NM, SP and RS acknowledge the DAE-DST, Govt. of India funding under the mega-science project – “Indian participation in the ALICE experiment at CERN” bearing Project No. SR/MF/PS-02/2021- IITI (E-37123). GGB acknowledges the Hungarian National Research, Development and Innovation Office (NKFIH) under Contract No. OTKA K135515, 2021-4.1.2-NEMZ_KI-2024- 00031, 2024-1.2.5-TÉT-2024-00022; Wigner Scientific Computing Laboratory (WSCLAB). The MoU between IIT Indore and HUN-REN Wigner RCP, Hungary, for the techno-scientific cooperation is highly appreciated. ANM would like to thank UCRD, Chandigarh University for their research facilities.

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