Efficient emulation of relativistic heavy ion collisions with transfer learning

Liyanage, D.; Ji, Y.; Everett, D.; Heffernan, M.; Heinz, U.; Mak, S.; Paquet, J. -F.

doi:10.1103/physrevc.105.034910

Title: Efficient emulation of relativistic heavy ion collisions with transfer learning

Journal Article · Mon Mar 28 00:00:00 UTC 2022 · Physical Review. C

DOI: https://doi.org/10.1103/physrevc.105.034910 · OSTI ID:1979870

^[1]; Ji, Y. ^[2]; Everett, D. ^[3];

^[4];

^[3]; Mak, S. ^[2];

^[2]

The Ohio State Univ., Columbus, OH (United States); OSTI
Duke Univ., Durham, NC (United States)
The Ohio State Univ., Columbus, OH (United States)
McGill Univ., Montreal, QC (Canada)

Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different collision systems and methodically account for experimental and theoretical uncertainties. Such studies require a vast number of costly numerical simulations. While computationally inexpensive surrogate models (“emulators”) can be used to efficiently approximate the predictions of heavy ion simulations across a broad range of model parameters, training a reliable emulator remains a computationally expensive task. We use transfer learning to map the parameter dependencies of one model emulator onto another, leveraging similarities between different simulations of heavy ion collisions. By limiting the need for large numbers of simulations to only one of the emulators, this technique reduces the numerical cost of comprehensive uncertainty quantification when studying multiple collision systems and exploring different models.

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Research Organization:: Duke Univ., Durham, NC (United States); The Ohio State Univ., Columbus, OH (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Nuclear Physics (NP)

Grant/Contract Number:: FG02-05ER41367; SC0004286

OSTI ID:: 1979870

Journal Information:: Physical Review. C, Journal Name: Physical Review. C Journal Issue: 3 Vol. 105; ISSN 2469-9985

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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