A general approach to maximise information density in neutron reflectometry analysis

TL;DR

This paper introduces a Bayesian evidence-based framework to optimize parameter selection in neutron reflectometry analysis, enhancing information density and reducing model ambiguity in interfacial material studies.

Contribution

It presents a robust, generic method for selecting free parameters based on Bayesian evidence, improving model reliability in reflectometry data analysis.

Findings

Framework successfully identifies optimal parameter sets

Application to phospholipid monolayer demonstrates effectiveness

Encourages routine consideration of model evidence in analysis

Abstract

Neutron and X-ray reflectometry are powerful techniques facilitating the study of the structure of interfacial materials. The analysis of these techniques is ill-posed in nature requiring the application of a model-dependent methods. This can lead to the over- and under- analysis of experimental data, when too many or too few parameters are allowed to vary in the model. In this work, we outline a robust and generic framework for the determination of the set of free parameters that is capable of maximising the in-formation density of the model. This framework involves the determination of the Bayesian evidence for each permutation of free parameters; and is applied to a simple phospholipid monolayer. We believe this framework should become an important component in reflectometry data analysis, and hope others more regularly consider the relative evidence for their analytical models.