A general approach to maximise information density in neutron reflectometry analysis
arXiv:1910.10581 · doi:10.1088/2632-2153/ab94c4
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.
References in corpus (3)
Cited by in corpus (7)
- Elucidating proximity magnetism through polarized neutron reflectometry and machine learning
- Determining the maximum information gain and optimising experimental design in neutron reflectometry using the Fisher information
- Advice on describing Bayesian analysis of neutron and X-ray reflectometry
- Probabilistic Parameter Estimation Using a Gaussian Mixture Density Network: Application to X-ray Reflectivity Data Curve Fitting
- The benefits of a Bayesian analysis for the characterization of magnetic nanoparticles
- Determining the Proximity Effect Induced Magnetic Moment in Graphene by Polarized Neutron Reflectivity and X-ray Magnetic Circular Dichroism
- Optimal weights and priors in simultaneous fitting of multiple small-angle scattering datasets