Deep Learning For X-Ray Or Neutron Scattering Under Grazing-Incidence: Extraction Of Distributions

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.