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Bayesian optimization in ab initio nuclear physics

Journal article
Authors A. Ekstrom
C. Forssen
C. Dimitrakakis
D. Dubhashi
H. T. Johansson
A. S. Muhammad
H. Salomonsson
Alexander Schliep
Published in Journal of Physics G-Nuclear and Particle Physics
Volume 46
Issue 9
ISSN 0954-3899
Publication year 2019
Published at Department of Computer Science and Engineering, Computing Science (GU)
Language en
Links dx.doi.org/10.1088/1361-6471/ab2b14
Keywords Bayesian optimization, nuclear physics, nucleon-nucleon scattering, effective field theory, formalism, Physics, app hp, 1957, physical review, v105, p302, oks vgj, 1993, physical review c, v48, p792
Subject categories Computer and Information Science

Abstract

Theoretical models of the strong nuclear interaction contain unknown coupling constants (parameters) that must be determined using a pool of calibration data. In cases where the models are complex, leading to time consuming calculations, it is particularly challenging to systematically search the corresponding parameter domain for the best fit to the data. In this paper, we explore the prospect of applying Bayesian optimization to constrain the coupling constants in chiral effective field theory descriptions of the nuclear interaction. We find that Bayesian optimization performs rather well with low-dimensional parameter domains and foresee that it can be particularly useful for optimization of a smaller set of coupling constants. A specific example could be the determination of leading three-nucleon forces using data from finite nuclei or three-nucleon scattering experiments.

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