Neural network emulation of flow in heavy-ion collisions at intermediate energies

Cox, Nicholas; Grundler, Xavier; Li, Bao-An

doi:10.1103/physrevc.110.044604

Neural network emulation of flow in heavy-ion collisions at intermediate energies

Journal Article · Fri Oct 11 00:00:00 EDT 2024 · Physical Review. C

DOI:https://doi.org/10.1103/physrevc.110.044604· OSTI ID:2531312

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Texas A&M University-Commerce, TX (United States); East Texas A&M University (formerly known as Texas A&M University-Commerce)
Texas A&M University-Commerce, TX (United States)

Applications of new techniques in machine learning are speeding up progress in research in various fields. In this work, we construct and evaluate a deep neural network (DNN) to be used within a Bayesian statistical framework as a faster and more reliable alternative to the Gaussian process (GP) emulator of an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model simulator of heavy-ion reactions at intermediate beam energies. We found strong evidence of the DNN being able to emulate the IBUU simulator's prediction on the strengths of protons' directed and elliptical flow very efficiently even with small training datasets and with accuracy about ten times higher than the GP. Here, limitations of our present work and future improvements are also discussed.

View Accepted Manuscript (DOE)

Research Organization:: East Texas A&M University, Commerce, TX (United States)

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

Grant/Contract Number:: SC0013702

OSTI ID:: 2531312

Alternate ID(s):: OSTI ID: 2474304

Journal Information:: Physical Review. C, Journal Name: Physical Review. C Journal Issue: 4 Vol. 110; ISSN 2469-9985

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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