A general approach to maximise information density in neutron reflectometry analysis. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: McCluskey, Andrew R; Cooper, Joshaniel F K; Arnold, Tom; Snow, Tim
• Is Part Of: Machine learning: science and technology. Volume 1:Issue 3(2020)
• Publication Date: 2020-07-02
• Subjects: Bayesian -- reflectometry -- neutron -- x-ray -- reflectivity -- reflection -- evidence; 006.31
• DOI: 10.1088/2632-2153/ab94c4
• Languages: English
• ISSNs: 2632-2153

Abstract

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 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 are capable of maximising the information 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.