Author: Calder, S.
Paper Title Page
WEPV022 Sample Alignment in Neutron Scattering Experiments Using Deep Neural Network 686
  • J.P. Edelen, K. Bruhwiler, A. Diaw, C.C. Hall
    RadiaSoft LLC, Boulder, Colorado, USA
  • S. Calder
    ORNL RAD, Oak Ridge, Tennessee, USA
  • C.M. Hoffmann
    ORNL, Oak Ridge, Tennessee, USA
  Funding: DOE Office of Science Office of Basic Energy Science SBIR award number DE-SC0021555
Access to neutron scattering centers, such as Oak Ridge National Laboratory (ORNL) and the NIST Center for Neutron Research, has provided beam energies to investigating a wide variety of applications such as particle physics, material science, and biology. In these experiments, the quality of collected data is very sensitive to sample and beam alignment, and stabilization of the experimental environment, requiring human intervention to tune the beam. While this procedure works, it is inefficient and time-consuming. In the work we present progress towards using machine learning to automate the alignment of a beamline in neutron scattering experiments. Our algorithm uses convolutional neural network to both learn a surrogate of the image data of the sample and to predict the sample contour using a u-net. We tested our algorithm on neutron camera images from the H2-BA powder diffractometer and the Topaz single crystal diffractometer beamlines of ORNL.
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About • Received ※ 10 October 2021       Revised ※ 22 October 2021       Accepted ※ 21 December 2021       Issue date ※ 06 February 2022
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