Bayesian optimization in ab initio nuclear physics
This work addresses a challenge in ab initio nuclear physics for researchers needing to calibrate models with time-consuming calculations, though it is incremental as it adapts an existing optimization method to this domain.
The paper tackled the problem of efficiently determining unknown coupling constants in complex nuclear interaction models by applying Bayesian optimization, finding it performs well for low-dimensional parameter domains and could be useful for optimizing smaller sets of constants.
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.