.png){kind=icon}
Return
Bayesian optimization in ab initio nuclear physics
.png){kind=icon}
OA
AI
DOI：10.1088/1361-6471/ab2b14.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Bayesian optimization
nuclear physics
nucleon-nucleon scattering
effective field theory
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3.5
Papers:
6.8K
Citations:
7.7K
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
View more