Author: Bruhwiler, K.
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.
 
poster icon Poster WEPV022 [4.472 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV022  
About • Received ※ 10 October 2021       Revised ※ 22 October 2021       Accepted ※ 21 December 2021       Issue date ※ 06 February 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)  
 
THAL04 Machine Learning Based Tuning and Diagnostics for the ATR Line at BNL 803
 
  • J.P. Edelen, K. Bruhwiler, E.G. Carlin, C.C. Hall
    RadiaSoft LLC, Boulder, Colorado, USA
  • K.A. Brown, V. Schoefer
    BNL, Upton, New York, USA
 
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682.
Over the past several years machine learning has increased in popularity for accelerator applications. We have been exploring the use of machine learning as a diagnostic and tuning tool for transfer line from the AGS to RHIC at Brookhaven National Laboratory. In our work, inverse models are used to either provide feed-forward corrections for beam steering or as a diagnostic to illuminate quadrupole magnets that have excitation errors. In this talk we present results on using machine learning for beam steering optimization for a range of different operating energies. We also demonstrate the use of inverse models for optical error diagnostics. Our results are from studies that use combine simulation and measurement data.
 
slides icon Slides THAL04 [4.845 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL04  
About • Received ※ 10 October 2021       Revised ※ 22 October 2021       Accepted ※ 06 February 2022       Issue date ※ 01 March 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)