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O: Fachverband Oberflächenphysik
O 86: Mini-Symposium: Machine learning applications in surface science II
O 86.5: Talk
Thursday, March 4, 2021, 12:00–12:15, R1
Neural Network Analysis of Neutron and X-Ray Reflectivity Data: Pathological Cases, Performance and Perspectives — •Alessandro Greco1, Vladimir Starostin1, Alexander Hinderhofer1, Alexander Gerlach1, Maximilian Skoda2, Stefan Kowarik3, and Frank Schreiber1 — 1Institute of Applied Physics, University of Tübingen, Germany — 2Rutherford Appleton Lab, ISIS Neutron and Muon Source, UK — 3Department of Physical Chemistry, University of Graz, Austria
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. Having demonstrated the general applicability of neural networks to analyze XRR and NR data before [1], this work 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). 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 discuss the effect of different scattering length density combinations on the prediction accuracy. The results are demonstrated using simulated data of a single-layer system.
[1] Greco et al., J. Appl. Cryst., 52, 1342 (2019)