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Extraction of Physical Parameters from X-ray Spectromicroscopy Data Using Machine Learning

Published online by Cambridge University Press:  10 August 2018

Yuta Suzuki ,
Hideitsu Hino ,
Tetsuro Ueno ,
Yasuo Takeichi ,
Masato Kotsugi  and
Kanta Ono
Yuta Suzuki
Affiliation:
Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan. High Energy Accelerator Research Organization, Institute of Materials Structure Science, Ibaraki, Japan.
Hideitsu Hino
Affiliation:
The Institute of Statistical Mathematics, Tokyo, Japan.
Tetsuro Ueno
Affiliation:
National Institutes for Quantum and Radiological Science and Technology, Hyogo, Japan
Yasuo Takeichi
Affiliation:
High Energy Accelerator Research Organization, Institute of Materials Structure Science, Ibaraki, Japan.
Masato Kotsugi
Affiliation:
Tokyo University of Science, Department of Materials Science and Technology, Tokyo, Japan.
Kanta Ono*
Affiliation:
High Energy Accelerator Research Organization, Institute of Materials Structure Science, Ibaraki, Japan.
*
* Corresponding author, [email protected]

Abstract

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Information
Microscopy and Microanalysis , Volume 24 , Supplement S2: Proceedings of the 14th International Conference on X-ray Microscopy (XRM2018) , August 2018 , pp. 478 - 479
Copyright
© Microscopy Society of America 2018 

References

[1] Takeichi, Y, et al, Rev. Sci. Instrum. 87 2016 013704.CrossRefGoogle Scholar
[2] Lerotic, M, et al, J. Electron Spectrosc. Relat. Phenom. 144-147 2005) p. 1137–1143.CrossRefGoogle Scholar
[3] Lerotic, M, et al, J. Synchrotron Rad. 21 2014) p. 1206–1212.CrossRefGoogle Scholar
[4] Ueno, T, et al, npj Comput. Mater. 4 2018) p. 4.CrossRefGoogle Scholar
[5] Groot, F Kotani, A in Core Level Spectroscopy of Solids. CRC Press Boca Raton 2008) p. 225–285.CrossRefGoogle Scholar
[6] Bishop, C M in Pattern Recognition and Machine Learning. Springer-Verlag New York 2006) p. 559–603.Google Scholar
[7] Stavitski, E de Groot, F M F Micron 41 2010) p. 687–694.CrossRefGoogle Scholar
[8] Shirley, E L Phys. Rev. Lett. 80 1998) p. 794–797.CrossRefGoogle Scholar
[9] Vinson, J, et al, Phys. Rev. B 83 2011) p. 115106 1-7.CrossRefGoogle Scholar
[10] Liang, Y., et al, Phys. Rev. Lett. 118 2017) p. 096402 1-7.CrossRefGoogle Scholar
[11] Borg, I Groenen, P in "Modern multidimensional scaling: theory and applications". Springer-Verlag New York 1997) p. 1–18.CrossRefGoogle Scholar
[12] Tenenbaum, J B Science 290 2000) p. 2319–2323.CrossRefGoogle Scholar
[13] Maaten, L V D Hinton, G J. Mach. Learn. Res. 9 2008) p. 2579–2605.Google Scholar
[14] This work was partly supported by JST CREST JPMJCR1761.Google Scholar