Deep learning for flow observables in high energy heavy-ion collisions
Abstract
We demonstrate how deep convolutional neural networks can be trained to predict 2+1 D hydrodynamic simulation results for flow coefficients, mean-pT and charged particle multiplicity from the initial energy density profile. We show that this method provides results that are accurate enough, so that one can use neural networks to reliably estimate multi-particle flow correlators. Additionally, we train networks that can take any model parameter as an additional input and demonstrate with a few examples that the accuracy remains good. The usage of neural networks can reduce the computation time needed in performing Bayesian analyses with multi-particle flow correlators by many orders of magnitude.
Main Authors
Format
Conferences
Conference paper
Published
2024
Series
Subjects
Publication in research information system
Publisher
EDP Sciences
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202408075413Use this for linking
Parent publication ISBN
978-2-7598-9126-9
Review status
Peer reviewed
ISSN
2101-6275
DOI
https://doi.org/10.1051/epjconf/202429602002
Conference
International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions
Language
English
Published in
EPJ Web of Conferences
Is part of publication
30th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions (Quark Matter 2023)
Citation
- Hirvonen, H., Eskola, K. J., & Niemi, H. (2024). Deep learning for flow observables in high energy heavy-ion collisions. In R. Bellwied, F. Geurts, R. Rapp, C. Ratti, A. Timmins, & I. Vitev (Eds.), 30th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions (Quark Matter 2023) (Article 02002). EDP Sciences. EPJ Web of Conferences, 296. https://doi.org/10.1051/epjconf/202429602002
License
Funder(s)
Research Council of Finland
European Commission
European Commission
Research Council of Finland
Funding program(s)
Academy Project, AoF
RIA Research and Innovation Action, H2020
ERC Advanced Grant
Centre of Excellence, AoF
Akatemiahanke, SA
RIA Research and Innovation Action, H2020
ERC Advanced Grant
Huippuyksikkörahoitus, SA
Additional information about funding
We acknowledge the financial support from the Jenny and Antti Wihuri Foundation, and the Academy of Finland project 330448. This research was funded as a part of the Center of Excellence in Quark Matter of the Academy of Finland (project 346325), the European Research Council project ERC-2018-ADG-835105 YoctoLHC, and the European Union’s Horizon 2020 research and innovation program under grant agreement No 824093 (STRONG-2020). The Finnish IT Center for Science (CSC) is acknowledged for the computing time through the Project jyy2580
Copyright© 2024 the Authors