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@INPROCEEDINGS{Schmidt:910111,
      author       = {Schmidt, Norberto and Abbate, O. I. and Prieto, Z. M. and
                      Robledo, J. I. and Márquez Damián, J. I. and Márquez, A.
                      A. and Dawidowski, J. and Mauerhofer, E. and Gutberlet, T.
                      and Brückel, T.},
      title        = {{N}eutron instruments simulations using machine learning
                      techniques with {KDS}ource},
      reportid     = {FZJ-2022-03619},
      year         = {2022},
      abstract     = {The design of neutron instruments usually is related to the
                      simulations of neutron beams. These simulations are normally
                      decoupled from the neutron source since the nuclear
                      reactions that govern the generation of neutrons in the
                      source are independent of the specific interactions that
                      take place in the neutron beam path. Also, neutron beams are
                      usually transported far away from the source to reduce the
                      background signal in the measurements and the radiation dose
                      of the personnel. When evaluating the neutron beam under
                      different operating conditions, it is useful to have a
                      source that can be re-sampled. KDSource [1,2] is an
                      open-source code that uses the adaptive multivariate Kernel
                      Density Estimation (KDE) method to estimate the source
                      distribution at a given point in the beam trajectory, which
                      seeks to overcome limitations of other variance-reduction
                      techniques. The approach presents a novel methodology to
                      optimize source modelling, which may be especially suited
                      for neutron beam and radiation shielding simulations. The
                      core idea of the methodology is to use some machine learning
                      libraries and algorithms to optimize the bandwidth selection
                      for each source variable. With this strategy, smooth
                      estimates of the variable distributions may be obtained from
                      particle lists at a given point in a simulation that
                      maintains correlations among all the variables. The code
                      implements the proposed methodology in Python, and it
                      consists of a module for KDE model optimization, and another
                      for sampling (i.e. generating new particles using the
                      previously optimized model). This work aims to present the
                      KDSource code with some usage examples to design the neutron
                      imaging instruments for the HBS project. [1] N.S. Schmidt,
                      O.I. Abbate, Z.M. Prieto, J.I. Robledo, J.I. Márquez
                      Damián, A.A. Márquez, J. Dawidowski, 2022. KDSource, a
                      tool for the generation of Monte Carlo particle sources
                      using kernel density estimation. Ann. Nucl. Energy 177. URL:
                      https://doi.org/10.1016/j.anucene.2022.109309 [2] O.I.
                      Abbate, N.S. Schmidt, Z.M. Prieto, J.I. Robledo, J.I.
                      Márquez Damián, A.A. Márquez, J. Dawidowski, 2021.
                      KDSource, a tool for the generation of Monte Carlo particle
                      sources using kernel density estimation. GitHub repository.
                      URL: https://github.com/KDSource/KDSource},
      month         = {Oct},
      date          = {2022-10-11},
      organization  = {JCNS WORKSHOP 2022 TRENDS AND
                       PERSPECTIVES IN NEUTRON SCATTERING:
                       EXPERIMENTS AND DATA ANALYSIS IN THE
                       DIGITAL AGE, Evangelische Akademie
                       Tutzing (Germany), 11 Oct 2022 - 14 Oct
                       2022},
      subtyp        = {Invited},
      cin          = {JCNS-2 / JCNS-HBS / PGI-4 / JARA-FIT},
      cid          = {I:(DE-Juel1)JCNS-2-20110106 / I:(DE-Juel1)JCNS-HBS-20180709
                      / I:(DE-Juel1)PGI-4-20110106 / $I:(DE-82)080009_20140620$},
      pnm          = {632 - Materials – Quantum, Complex and Functional
                      Materials (POF4-632) / 6G4 - Jülich Centre for Neutron
                      Research (JCNS) (FZJ) (POF4-6G4)},
      pid          = {G:(DE-HGF)POF4-632 / G:(DE-HGF)POF4-6G4},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/910111},
}