Open AccessNeural network analysis of neutron and x-ray reflectivity data: pathological cases, performance and perspectives
Author(s) -
Alessandro Greco,
Vladimir Starostin,
Alexander Hinderhofer,
Alexander Gerlach,
Maximilian W. A. Skoda,
Stefan Kowarik,
Frank Schreiber
Publication year - 2021
Publication title -
machine learning: science and technology
Language(s) - English
Resource type - Journals
ISSN - 2632-2153
DOI - 10.1088/2632-2153/abf9b1
Subject(s) - x ray reflectivity , neutron reflectometry , reflectometry , reflection (computer programming) , neutron , noise (video) , artificial neural network , neutron scattering , scattering , computer science , optics , enhanced data rates for gsm evolution , materials science , signal (programming language) , computational physics , physics , small angle neutron scattering , artificial intelligence , reflectivity , image (mathematics) , computer vision , nuclear physics , time domain , programming language
Neutron and x-ray reflectometry (NR and XRR) are powerful techniques to investigate the structural, morphological and even magnetic properties of solid and liquid thin films. While neutrons and x-rays behave similarly in many ways and can be described by the same general theory, they fundamentally differ in certain specific aspects. These aspects can be exploited to investigate different properties of a system, depending on which particular questions need to be answered. Having demonstrated the general applicability of neural networks to analyze XRR and NR data before (Greco et al 2019 J. Appl. Cryst. 52 1342), this study discusses challenges arising from certain pathological cases as well as performance issues and perspectives. These cases include a low signal-to-noise ratio, a high background signal (e.g. from incoherent scattering), as well as a potential lack of a total reflection edge (TRE). By dynamically modifying the training data after every mini batch, a fully-connected neural network was trained to determine thin film parameters from reflectivity curves. We show that noise and background intensity pose no significant problem as long as they do not affect the TRE. However, for curves without strong features the prediction accuracy is diminished. Furthermore, we compare the prediction accuracy for different scattering length density combinations. The results are demonstrated using simulated data of a single-layer system while also discussing challenges for multi-component systems.