TY  - JOUR
AU  - Van Herck, Walter
AU  - Fisher, Jonathan
AU  - Ganeva, Marina
TI  - Deep learning for X-ray or neutron scattering under grazing-incidence: extraction of distributions
JO  - Materials Research Express
VL  - 8
SN  - 2053-1591
CY  - Bristol
PB  - IOP Publ.
M1  - FZJ-2021-00038
SP  - 04515
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000642500400001
DO  - DOI:10.1088/2053-1591/abd590
UR  - https://juser.fz-juelich.de/record/889108
ER  -