Early-Stopping of Scattering Pattern Observation with Bayesian Modeling


  • Akinori Asahara Hitachi Ltd.
  • Hidekazu Morita Hitachi Ltd.
  • Chiharu Mitsumata National Institute for Materials Science
  • Kanta Ono High Engergy Accelerator Research Organization
  • Masao Yano Toyota Motor Corporation
  • Tetsuya Shoji Toyota Motor Corporation




This paper describes a new machine-learning application to speed up Small-angle neutron scattering (SANS) experiments, and its method based on probabilistic modeling. SANS is one of the scattering experiments to observe microstructures of materials; in it, two-dimensional patterns on a plane (SANS pattern) are obtained as measurements. It takes a long time to obtain accurate experimental results because the SANS pattern is a histogram of detected neutrons. For shortening the measurement time, we propose an earlystopping method based on Gaussian mixture modeling with a prior generated from B-spline regression results. An experiment using actual SANS data was carried out to examine the accuracy of the method. It was confirmed that the accuracy with the proposed method converged 4 minutes after starting the experiment (normal SANS takes about 20 minutes).




How to Cite

Asahara, A., Morita, H., Mitsumata, C., Ono, K., Yano, M., & Shoji, T. (2019). Early-Stopping of Scattering Pattern Observation with Bayesian Modeling. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9410-9415. https://doi.org/10.1609/aaai.v33i01.33019410



IAAI Technical Track: Emerging Papers