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@ARTICLE{Robledo:1028488,
      author       = {Robledo, José Ignacio and Frielinghaus, Henrich and
                      Willendrup, Peter and Lieutenant, Klaus},
      title        = {{L}earning from virtual experiments to assist users of
                      {S}mall {A}ngle {N}eutron {S}cattering in model selection},
      journal      = {Scientific reports},
      volume       = {14},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2024-04640},
      pages        = {14996},
      year         = {2024},
      abstract     = {In this work, we combine the advantages of virtual Small
                      Angle Neutron Scattering (SANS) experiments carried out by
                      Monte Carlo simulations with the recent advances in computer
                      vision to generate a tool that can assist SANS users in
                      small angle scattering model selection. We generate a
                      dataset of almost 260.000 SANS virtual experiments of the
                      SANS beamline KWS-1 at FRM-II, Germany, intended for Machine
                      Learning purposes. Then, we train a recommendation system
                      based on an ensemble of Convolutional Neural Networks to
                      predict the SANS model from the two-dimensional scattering
                      pattern measured at the position-sensitive detector of the
                      beamline. The results show that the CNNs can learn the model
                      prediction task, and that this recommendation system has a
                      high accuracy in the classification task on 46 different
                      SANS models. We also test the network with real data and
                      explore the outcome. Finally, we discuss the reach of
                      counting with the set of virtual experimental data presented
                      here, and of such a recommendation system in the SANS user
                      data analysis procedure.},
      cin          = {JCNS-FRM-II / JCNS-2 / JCNS-4 / MLZ},
      ddc          = {600},
      cid          = {I:(DE-Juel1)JCNS-FRM-II-20110218 /
                      I:(DE-Juel1)JCNS-2-20110106 / I:(DE-Juel1)JCNS-4-20201012 /
                      I:(DE-588b)4597118-3},
      pnm          = {6G4 - Jülich Centre for Neutron Research (JCNS) (FZJ)
                      (POF4-6G4) / 632 - Materials – Quantum, Complex and
                      Functional Materials (POF4-632)},
      pid          = {G:(DE-HGF)POF4-6G4 / G:(DE-HGF)POF4-632},
      experiment   = {EXP:(DE-MLZ)KWS1-20140101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38951158},
      UT           = {WOS:001260844500034},
      doi          = {10.1038/s41598-024-65712-y},
      url          = {https://juser.fz-juelich.de/record/1028488},
}