Vacuum and Surface Science
Online ISSN : 2433-5843
Print ISSN : 2433-5835
Special Feature : Research Forefront of Surface and Vacuum Science Developed by Data-Driven Approach
Efficiency Improvement of X-Ray Spectroscopy Experiment by Machine Learning
Tetsuro UENOHideitsu HINOKanta ONO
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2019 Volume 62 Issue 3 Pages 147-152

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Abstract

We present an adaptive design of experiment (DoE) by machine learning for X-ray spectroscopy to improve its efficiency. One of the machine learning techniques, Gaussian process regression predicts a spectrum from the experimental data and determines the optimal energy points to measure. Adaptive DoE successfully reduces total energy points to measure as compared to an X-ray magnetic circular dichroism spectroscopy experiment by a conventional DoE. This method has potential applicability to various measurements and reduces the time and cost of experiments.

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この記事はクリエイティブ・コモンズ [表示 - 非営利 4.0 国際]ライセンスの下に提供されています。
https://creativecommons.org/licenses/by-nc/4.0/deed.ja
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