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@ARTICLE{CarmonaLoaiza:888899,
      author       = {Carmona-Loaiza, Juan Manuel},
      title        = {{T}owards {R}eflectivity profile inversion through
                      {A}rtificial {N}eural {N}etworks},
      reportid     = {FZJ-2020-05304},
      year         = {2020},
      note         = {Submitted to MLST (Machine Learning: Science and
                      Technology) - 10 pages, 8 figures},
      abstract     = {The goal of Specular Neutron and X-ray Reflectometry is to
                      infer materials Scattering Length Density (SLD) profiles
                      from experimental reflectivity curves. This paper focuses on
                      investigating an original approach to the ill-posed
                      non-invertible problem which involves the use of Artificial
                      Neural Networks (ANN). In particular, the numerical
                      experiments described here deal with large data sets of
                      simulated reflectivity curves and SLD profiles, and aim to
                      assess the applicability of Data Science and Machine
                      Learning technology to the analysis of data generated at
                      large scale facilities. It is demonstrated that, under
                      certain circumstances, properly trained Deep Neural Networks
                      are capable of correctly recovering plausible SLD profiles
                      when presented with never-seen-before simulated reflectivity
                      curves. When the necessary conditions are met, a proper
                      implementation of the described approach would offer two
                      main advantages over traditional fitting methods when
                      dealing with real experiments, namely, 1. no prior
                      assumptions about the sample physical model are required and
                      2. the times-to-solution are shrank by orders of magnitude,
                      enabling faster batch analyses for large datasets.},
      cin          = {JCNS-FRM-II / MLZ},
      cid          = {I:(DE-Juel1)JCNS-FRM-II-20110218 / I:(DE-588b)4597118-3},
      pnm          = {6G4 - Jülich Centre for Neutron Research (JCNS) (POF3-623)
                      / 6G15 - FRM II / MLZ (POF3-6G15)},
      pid          = {G:(DE-HGF)POF3-6G4 / G:(DE-HGF)POF3-6G15},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2010.07634},
      howpublished = {arXiv:2010.07634},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2010.07634;\%\%$},
      url          = {https://juser.fz-juelich.de/record/888899},
}