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000889108 1001_ $$0P:(DE-Juel1)145760$$aVan Herck, Walter$$b0
000889108 245__ $$aDeep learning for X-ray or neutron scattering under grazing-incidence: extraction of distributions
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000889108 520__ $$aGrazing-incidence small-angle scattering (GISAS) is a technique of significant importance for the investigation of thin multilayered films containing nano-sized objects. It provides morphology information averaged over the sample area. However, this averaging together with multiple reflections and the well-known phase problem make the data analysis challenging and time consuming. In the present paper we show that densely connected neural networks (DenseNets) can be applied for GISAS data analysis and deliver fast and plausible results. The extraction of the rotational distributions of hexagonal nanoparticle arrangements is taken as a case study.
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000889108 7001_ $$0P:(DE-Juel1)169150$$aFisher, Jonathan$$b1
000889108 7001_ $$0P:(DE-Juel1)161101$$aGaneva, Marina$$b2$$eCorresponding author
000889108 773__ $$0PERI:(DE-600)2760382-9$$a10.1088/2053-1591/abd590$$p04515$$tMaterials Research Express$$v8$$x2053-1591$$y2021
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