Journal Article

FZJ-2021-00038

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Deep learning for X-ray or neutron scattering under grazing-incidence: extraction of distributions

Van Herck, W. ; Fisher, J. ; Ganeva, M. (Corresponding author)FZJ*

2021
IOP Publ. Bristol

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Please use a persistent id in citations: http://hdl.handle.net/2128/27685  doi:10.1088/2053-1591/abd590

Abstract: Grazing-incidence small-angle scattering (GISAS) is a technique of significant importance for the investigation of thin multilayered films containing nano-sized objects. It provides morphology information averaged over the sample area. However, this averaging together with multiple reflections and the well-known phase problem make the data analysis challenging and time consuming. In the present paper we show that densely connected neural networks (DenseNets) can be applied for GISAS data analysis and deliver fast and plausible results. The extraction of the rotational distributions of hexagonal nanoparticle arrangements is taken as a case study.

Keyword(s): Instrument and Method Development (1st) ; Instrument and Method Development (2nd)

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Contributing Institute(s):

1. JCNS-FRM-II (JCNS-FRM-II)
2. Heinz Maier-Leibnitz Zentrum (MLZ)

Research Program(s):

1. 6G4 - Jülich Centre for Neutron Research (JCNS) (FZJ) (POF4-6G4) (POF4-6G4)
2. 623 - Data Management and Analysis (POF4-623) (POF4-623)

Experiment(s):

1. SCG: Scientific Computing Group

Appears in the scientific report 2021

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