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@ARTICLE{VanHerck:889108,
      author       = {Van Herck, Walter and Fisher, Jonathan and Ganeva, Marina},
      title        = {{D}eep learning for {X}-ray or neutron scattering under
                      grazing-incidence: extraction of distributions},
      journal      = {Materials Research Express},
      volume       = {8},
      issn         = {2053-1591},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {FZJ-2021-00038},
      pages        = {04515},
      year         = {2021},
      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.},
      cin          = {JCNS-FRM-II / MLZ},
      ddc          = {620},
      cid          = {I:(DE-Juel1)JCNS-FRM-II-20110218 / I:(DE-588b)4597118-3},
      pnm          = {6G4 - Jülich Centre for Neutron Research (JCNS) (FZJ)
                      (POF4-6G4) / 623 - Data Management and Analysis (POF4-623)},
      pid          = {G:(DE-HGF)POF4-6G4 / G:(DE-HGF)POF4-623},
      experiment   = {EXP:(DE-MLZ)SCG-20150203},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000642500400001},
      doi          = {10.1088/2053-1591/abd590},
      url          = {https://juser.fz-juelich.de/record/889108},
}