A4 Article in conference proceedings
A Deep Learning Based Estimator for Light Flavour Elliptic Flow in Heavy Ion Collisions at LHC Energies (2025)

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

JYU authors or editors

Publication details

All authors or editors: Barnaföldi, Gergely Gábor; Mallick, Neelkamal; Prasad, Suraj; Sahoo, Raghunath; Mishra, Aditya Nath

Parent publication: The 21st International Conference on Strangeness in Quark Matter (SQM 2024)

Parent publication editors: Cheshkov, C.; Guernane, R.; Maire, A.

Conference:

Place and date of conference: Strasbourg, France, 3.-7.6.2024

Journal or series: EPJ Web of Conferences

ISSN: 2101-6275

eISSN: 2100-014X

Publication year: 2025

Publication date: 27/01/2025

Number in series: 316

Article number: 03004

Publisher: EDP Sciences

Publication country: France

Publication language: English

DOI: https://doi.org/10.1051/epjconf/202531603004

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/99826

Web address of parallel published publication (pre-print): https://arxiv.org/abs/2409.19462

Abstract

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.

Keywords: particle physics; particle accelerators; simulation; estimating (statistical methods); machine learning; deep learning

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2025

Preliminary JUFO rating: 1