Year: 2022
Type: article
Abstract: Bayesian model mixing (BMM) is a statistical technique that can be used to combine models that are predictive in different input domains into a composite distribution that has improved predictive power over the entire input space. We explore the application of BMM to the mixing of two expansions of a function of a coupling constant $g$ that are valid at small and large values of $g$ respectively. This type of problem is quite common in nuclear physics, where physical properties are straightforwardly calculable in strong and weak interaction limits or at low and high densities or momentum transfers, but difficult to calculate in between. Interpolation between these limits is often accomplished by a suitable interpolating function, e.g., Pad\'e approximants, but it is then unclear how to quantify the uncertainty of the interpolant. We address this problem in the simple context of the partition function of zero-dimensional ${\ensuremath{\phi}}^{4}$ theory, for which the (asymptotic) expansion at small $g$ and the (convergent) expansion at large $g$ are both known. We consider three mixing methods: linear mixture BMM, localized bivariate BMM, and localized multivariate BMM with Gaussian processes. We find that employing a Gaussian process in the intermediate region between the two predictive models leads to the best results of the three methods. The methods and validation strategies we present here should be generalizable to other nuclear physics settings.
Source: Physical Review C
Institutions: Ohio University, The Ohio State University
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: G222768804, THEORETICAL AND COMPUTATIONAL STUDIES IN INTERMEDIATE ENERGYNUCLEAR PHYSICS, G4255810811, NUCLEAR DYNAMICS AND ASTROPHYSICS IN FEW AND MANY-BODY SYSTEMS, Frameworks: Bayesian Analysis of Nuclear Dynamics, Research in Low-Energy Nuclear Theory, Research in Low-Energy Nuclear Theory
OpenAlex ID: W4306925335