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Optimal Design of Experiment for X-Ray Spectromicroscopy by Machine Learning
https://repo.qst.go.jp/records/49423
https://repo.qst.go.jp/records/494232a88872e-6870-4569-91ac-a797b10ab838
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2019-02-28 | |||||
| タイトル | ||||||
| タイトル | Optimal Design of Experiment for X-Ray Spectromicroscopy by Machine Learning | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Ueno, Tetsuro
× Ueno, Tetsuro× Hino, Hideitsu× Ono, Kanta× 上野 哲朗 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | The total measurement time of an X-ray spectromicroscopy experiment using a scanning transmission X-ray microscope (STXM) is determined by a multiplication of a number of energy points ne, sample scanning points ns, and measurement time per each point tm plus overhead. Overhead consists of time for data acquisition, moving of sample scanners, beamline optics and undulator properties (gap and phase of magnet arrays). An X-ray spectromicroscopy experiment with an STXM is performed as an image acquisition by sample scanning in an energy-by-energy regime. Moreover, moving of beamline optics such as a grating and mirrors takes longer time than that of piezoelectric actuators for sample scanning. Therefore, it is a good strategy to reduce ne to reduce total measurement time. Another strategy to reduce total measurement time is an optimization of tm. One can reduce tm at the expense of a signal-to-noise (S/N) ratio of spectra, which is proportional to tm1/2. It is important to reduce total measurement time without degrading the quality of spectra to extract physical or chemical parameters by analysis. Machine learning techniques are expected to resolve this issue. Ueno et al. proposed the adaptive design of an X- ray magnetic circular dichroism (XMCD) spectroscopy experiment by Gaussian process (GP) modeling, a machine learning technique, and successfully reduced the total number of energy points to measure to evaluate magnetic moments with required accuracy. | |||||
| 書誌情報 |
Microscopy and Microanalysis 巻 24, 号 S2, p. 134-135, 発行日 2018-08 |
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| 出版者 | ||||||
| 出版者 | Cambridge University Press | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1431-9276 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1017/S1431927618013065 | |||||
| 関連サイト | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1017/S1431927618013065 | |||||
| 関連名称 | https://doi.org/10.1017/S1431927618013065 | |||||
