Early-Stopping of Scattering Pattern Observation with Bayesian Modeling
Abstract
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).
Cite
Text
Asahara et al. "Early-Stopping of Scattering Pattern Observation with Bayesian Modeling." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019410Markdown
[Asahara et al. "Early-Stopping of Scattering Pattern Observation with Bayesian Modeling." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/asahara2019aaai-early/) doi:10.1609/AAAI.V33I01.33019410BibTeX
@inproceedings{asahara2019aaai-early,
title = {{Early-Stopping of Scattering Pattern Observation with Bayesian Modeling}},
author = {Asahara, Akinori and Morita, Hidekazu and Mitsumata, Chiharu and Ono, Kanta and Yano, Masao and Shoji, Tetsuya},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {9410-9415},
doi = {10.1609/AAAI.V33I01.33019410},
url = {https://mlanthology.org/aaai/2019/asahara2019aaai-early/}
}