Towards automated analysis for neutron reflectivity

Abstract

We describe a neural network-based tool for the automatic estimation of thin film thicknesses and scattering length densities from neutron reflectivity curves. The neural network sits within a data pipeline, that takes raw data from a neutron reflectometer, and outputs data and parameter estimates into a fitting program for end user analysis. Our tool deals with simple cases, predicting the number of layers and layer parameters up to three layers on a bulk substrate. This provides good accuracy in parameter estimation, while covering a large portion of the use case. By automating steps in data analysis that only require semi-expert knowledge, we lower the barrier to on-experiment data analysis, allowing better utility to be made from large scale facility experiments. Transfer learning showed that our tool works for x-ray reflectivity, and all code is freely available on GitHub (neutron-net 2020, available at: https://github.com/xmironov/ neutron-net) (Accessed: 25 June 2020).