A Deep Learning Based Estimator for Light Flavour Elliptic Flow in Heavy Ion Collisions at LHC Energies
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 m...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/01/epjconf_sqm2024_03004.pdf |
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| Summary: | 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 collisions at various LHC energies. These results are compared with the available experimental data wherever possible. |
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| ISSN: | 2100-014X |