Bayesian optimization in ab initio nuclear physics

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.

33 pages, 14 figures

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Cited by in corpus (11)

• A Guided Tour of Ab Initio Nuclear Many-Body Theory
• Machine Learning in Nuclear Physics
• Three-Nucleon Forces: Implementation and Applications to Atomic Nuclei and Dense Matter
• New Ideas in Constraining Nuclear Forces
• Training and Projecting: A Reduced Basis Method Emulator for Many-Body Physics
• Physics-informed Gaussian Process for Online Optimization of Particle Accelerators
• Gaussian conversion protocols for cubic phase state generation
• Machine Learning for the Prediction of Converged Energies from Ab Initio Nuclear Structure Calculations
• Optimization and Supervised Machine Learning Methods for Fitting Numerical Physics Models without Derivatives
• Use of Bayesian Optimization to Understand the Structure of Nuclei
• Bayesian method for fitting the low-energy constants in chiral perturbation theory