Multimodal reconstruction of TbCo thin film structure with Basyeian analysis of polarised neutron reflectivity

TL;DR

This paper applies Bayesian analysis with Monte Carlo methods to polarised neutron reflectivity data of TbCo thin films, revealing multiple plausible structural solutions and emphasizing the importance of rigorous data analysis in neutron reflectometry.

Contribution

It introduces a Bayesian framework for neutron reflectivity analysis that uncovers multimodal solutions, improving the understanding of thin film structures beyond traditional fitting methods.

Findings

Multiple probable reconstructions were identified, highlighting multimodality.

Bayesian analysis enhances the reliability of structural parameters in neutron reflectivity.

Rigorous data analysis is crucial for poor statistically conditioned data.

Abstract

We implemented the Bayesian analysis to the polarised neutron reflectivity data. Reflectivity data from a magnetic TbCo thin film structure was studied using the bundle of a Monte-Carlo Markov-chain algorithm, likelihood estimation, and error modeling. By utilizing the Bayesian analysis, we were able to investigate the uniqueness of the solution beyond reconstructing the magnetic and structure parameters. This approach has demonstrated its expedience as several probable reconstructions were found (the multimodality case) concerning the isotopic composition of the surface cover layer. Such multimodal reconstruction emphasizes the importance of rigorous data analysis instead of the direct data fitting approach, especially in the case of poor statistically conditioned data, typical for neutron reflectivity experiments. The analysis details and the discussion on multimodality are in this article.