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 that should be used to rationalise reflectometry 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 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. arXiv:1910.10581v1 [cond-mat.soft]

The benefits of a Bayesian analysis for the characterization of magnetic nanoparticles

“…It can be noted, that e.g. also for the analysis of neutron reflectometry data [22] the maximum number of possible free fit parameters was found to be 4.…”

“…The benefit of this approach has already been demonstrated for the analysis of Rietveld refinement of X-ray and neutron diffraction patterns [18] , the structure analysis of macromolecules by small angle scattering (SAS) [19] as well as for the estimation of the particle size or the correlation lengths of colloidal nanocrystals by SANS [20,21] . More recently, the Bayesian approach was also used to optimize the refinement of neutron reflectometry data [22] .…”