Using Bayesian Model Selection to Advise Neutron Reflectometry Analysis from Langmuir-Blodgett Monolayers

Abstract

The analysis of neutron and X-ray reflectometry data is important for the study of interfacial soft matter structures. However, there is still substantial discussion regarding the analytical models<br>that should be used to rationalise relflectometry data. In this work, we outline a robust and generic framework for the determination of the evidence for a particular model given experimental data, by<br>applying Bayesian logic. We apply this framework to the study of Langmuir-Blodgett monolayers by considering three possible analytical models from a recently published investigation [Campbell et al., J. Colloid Interface Sci, 2018, 531, 98]. From this, we can determine which model has the most evidence given the experimental data, and show the effect that different isotopic contrasts of neutron reflectometry will have on this. We believe that this general framework could become an important component of neutron and X-ray reflectometry data analysis, and hope others more regularly consider the relative evidence for their analytical models.<br>