Probabilistic neural networks for improved analyses with phenomenological R -matrix

Kim, Chanhee H.; Chae, Kyungyuk Y.; Smith, Michael S.; Bardayan, Daniel W.; Brune, Carl R.; deBoer, R. J.; Lu, Dan; Odell, Daniel

doi:10.1103/physrevc.110.054609

Title: Probabilistic neural networks for improved analyses with phenomenological R -matrix

Journal Article · 17 November 2024 · Physical Review. C

DOI: https://doi.org/10.1103/physrevc.110.054609 · OSTI ID:2478324

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^[4]; deBoer, R. J. ^[3];

^[2]; Odell, Daniel ^[4]

Sungkyunkwan Univ., Suwon (Republic of Korea)
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
University of Notre Dame, IN (United States)
Ohio Univ., Athens, OH (United States)

Here we present a method for measurement analyses based on probabilistic deep neural networks that provide several advantages over conventional analyses with phenomenological models. These include predicting physical quantities directly from data, the rapid generation of statistically robust uncertainties, and the ability to bypass some parameters that may induce ambiguities and complications in data analysis. As deep learning methods make predictions through “black boxes,” the uncertainty quantification is typically challenging. We use a probabilistic framework that provides thorough uncertainty quantification and is straightforward to follow in practice. With the network architecture based on the Transformer, we demonstrate the current method for predicting nuclear resonance parameters from scattering data using the phenomenological R-matrix model.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: Institute for Basic Science; National Research Foundation of Korea (NRF); National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Nuclear Physics (NP)

Grant/Contract Number:: AC05-00OR22725; FG02-88ER40387; NA0004065

OSTI ID:: 2478324

Journal Information:: Physical Review. C, Journal Name: Physical Review. C Journal Issue: 5 Vol. 110; ISSN 2469-9985

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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Title: Probabilistic neural networks for improved analyses with phenomenological R -matrix

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