# Bayesian optimization in ab initio nuclear physics

## TL;DR

This paper investigates the use of Bayesian optimization to efficiently determine coupling constants in complex nuclear physics models, especially when traditional methods are computationally expensive.

## Contribution

It demonstrates that Bayesian optimization is effective for low-dimensional parameter spaces in nuclear physics models, aiding in the calibration of coupling constants.

## Key findings

- Bayesian optimization performs well with low-dimensional parameters.
- It can be useful for optimizing a small set of coupling constants.
- Potential application in determining three-nucleon forces.

## 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.