Deep learning for flow observables in ultrarelativistic heavy-ion collisions
Hirvonen, H., Eskola, K. J., & Niemi, H. (2023). Deep learning for flow observables in ultrarelativistic heavy-ion collisions. Physical Review C, 108, Article 034905. https://doi.org/10.1103/PhysRevC.108.034905
Julkaistu sarjassa
Physical Review CPäivämäärä
2023Tekijänoikeudet
© Authors. Published by the American Physical Society. Funded by SCOAP3.
We train a deep convolutional neural network to predict hydrodynamic results for flow coefficients, average transverse momenta, and charged particle multiplicities in ultrarelativistic heavy-ion collisions from the initial energy density profiles. We show that the neural network can be trained accurately enough so that it can reliably predict the hydrodynamic results for the flow coefficients and, remarkably, also their correlations like normalized symmetric cumulants, mixed harmonic cumulants, and flow-transverse-momentum correlations. At the same time the required computational time decreases by several orders of magnitude. To demonstrate the advantage of the significantly reduced computation time, we generate 107 initial energy density profiles from which we predict the flow observables using the neural network, which is trained using 5×103, and validated using 9×104 events per collision energy. We then show that increasing the number of collision events from 9×104 to 107 can have significant effects on certain statistics-expensive flow correlations, which should be taken into account when using these correlators as constraints in the determination of the quantum chromodynamics matter properties.
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Julkaisija
American Physical Society (APS)ISSN Hae Julkaisufoorumista
2469-9985Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/193445137
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Suomen AkatemiaRahoitusohjelmat(t)
Huippuyksikkörahoitus, SA; Akatemiahanke, SALisätietoja rahoituksesta
We acknowledge the financial support from the Jenny and Antti Wihuri Foundation, and the Academy of Finland Project No. 330448 (K.J.E.). This research was funded as a part of the Center of Excellence in Quark Matter of the Academy of Finland (Project No. 346325). This research is part of the European Research Council Project No. ERC-2018-ADG-835105 YoctoLHC. The Finnish IT Center for Science (CSC) is acknowledged for the computing time through Project No. jyy2580. ...Lisenssi
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