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説明:

There are a wide variety of measurement techniques that produce spectra as output datasets. Existing data analysis software and some open-source macros are useful but not sufficient for non-experts to perform peak-fitting analysis and interpretation. Therefore, we have investigated analysis method using unsupervised machine learning for highthroughput and automated peak deconvolution analysis of spectra without prior knowledge of experimental techniques and material databases. In this paper, we will introduce the open-source package called “EMPeaks”, including development chronology, specific usage, current issues, and analysis examples.

権利情報:

• In Copyright
  ©公益社団法人 日本表面真空学会

キーワード: spectroscopy, peak fitting, data analysis, machine learning

刊行年月日: 2024-10-10

出版者: Surface Science Society Japan

掲載誌:

• 表面と真空 (ISSN: 24335835) vol. 67 issue. 10 p. 500-505

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5257

公開URL: https://doi.org/10.1380/vss.67.500

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更新時刻: 2025-01-10 16:31:04 +0900

MDRでの公開時刻: 2025-01-10 16:31:04 +0900