Bayesian optimization in ab initio nuclear physics

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. Bayesian optimization in ab initio nuclear physics 2 1. Introduction Mathematical optimization plays a central role in natural science. Indeed, most theoretical predictions are preceded by a calibration stage whereby the parameters of the model are optimized to reproduce a selected set of calibration data. In nuclear physics, the coupling constants of any theory of the strong interaction between protons and neutrons (nucleons) must be determined from experimental data before one can attempt to solve the Schrödinger equation to make quantitative predictions of the properties of atomic nuclei. Typically, measured low-energy nucleon-nucleon (N N) cross sections and the properties of light nuclei with mass number A 4 have been used for calibrating the NN and three-nucleon (NNN) interaction sectors of the nuclear force, see e.g.