% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{TeixeiraParente:906184,
      author       = {Teixeira Parente, Mario and Schneidewind, Astrid and
                      Brandl, Georg and Franz, Christian and Noack, Marcus and
                      Boehm, Martin and Ganeva, Marina},
      title        = {{B}enchmarking {A}utonomous {S}cattering {E}xperiments
                      {I}llustrated on {TAS}},
      journal      = {Frontiers in Materials},
      volume       = {8},
      issn         = {2296-8016},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2022-01283},
      pages        = {772014},
      year         = {2022},
      abstract     = {With the advancement of artificial intelligence and machine
                      learning methods, autonomous approaches are recognized to
                      have great potential for performing more efficient
                      scattering experiments. In our view, it is crucial for such
                      approaches to provide thorough evidence about respective
                      performance improvements in order to increase acceptance
                      within a scientific community. Therefore, we propose a
                      benchmarking procedure designed as a cost-benefit analysis
                      that is applicable to any scattering method sequentially
                      collecting data during an experiment. For a given approach,
                      the performance assessment is based on how much benefit,
                      given a certain cost budget, it is able to acquire in
                      predefined test cases. Different approaches thus get a
                      chance for comparison and can make their advantages explicit
                      and visible. Key components of the procedure, i.e., cost
                      measures, benefit measures, and test cases, are made precise
                      for the setting of three-axes spectrometry (TAS) as an
                      illustration. Finally, we discuss neglected aspects and
                      possible extensions for the TAS setting and comment on the
                      procedure’s applicability to other scattering methods. A
                      Python implementation of the procedure to simplify its
                      utilization by interested researchers from the field is also
                      provided.},
      cin          = {JCNS-FRM-II / MLZ / JCNS-2 / JCNS-4},
      ddc          = {620},
      cid          = {I:(DE-Juel1)JCNS-FRM-II-20110218 / I:(DE-588b)4597118-3 /
                      I:(DE-Juel1)JCNS-2-20110106 / I:(DE-Juel1)JCNS-4-20201012},
      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)PANDA-20140101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000760612600001},
      doi          = {10.3389/fmats.2021.772014},
      url          = {https://juser.fz-juelich.de/record/906184},
}