期刊
JOURNAL OF PHYSICS G-NUCLEAR AND PARTICLE PHYSICS
卷 46, 期 9, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/1361-6471/ab2b14
关键词
Bayesian optimization; nuclear physics; nucleon-nucleon scattering; effective field theory
资金
- BigData@Chalmersinitiative
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme [758027]
- Swedish Research Council [2015-00225, 2017-04234]
- Marie Sklodowska Curie Actions [INCA 600398]
- Swedish Research Council [2015-00225, 2017-04234] Funding Source: Swedish Research Council
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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