Bayesian optimization in ab initio nuclear physics
Journal article, 2019

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.

effective field theory

nucleon-nucleon scattering

nuclear physics

Bayesian optimization

Author

Andreas Ekström

Chalmers, Physics, Subatomic and Plasma Physics

Christian Forssen

Chalmers, Physics, Subatomic and Plasma Physics

Christos Dimitrakakis

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Devdatt Dubhashi

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Håkan T Johansson

Chalmers, Physics, Subatomic and Plasma Physics

Muhammad Azam Sheikh

Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd, Data Science Research Engineers

Hans Salomonsson

Chalmers, Computer Science and Engineering (Chalmers), CSE Verksamhetsstöd

Alexander Schliep

University of Gothenburg

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Journal of Physics G: Nuclear and Particle Physics

0954-3899 (ISSN)

Vol. 46 9 095101

Subject Categories

Computational Mathematics

Other Physics Topics

Probability Theory and Statistics

DOI

10.1088/1361-6471/ab2b14

More information

Latest update

12/6/2019