Early-Stopping of Scattering Pattern Observation with Bayesian Modeling - Asahara, Akinori; Morita, Hidekazu; Mitsumata, Chiharu; Ono, Kanta; Yano, Masao; Shoji, Tetsuya (Proceedings of the AAAI Conference on Artificial Intelligence, 2019)
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).