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Description:

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.

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• In Copyright
  

  ©公益社団法人 日本表面真空学会

Keyword: spectroscopy, peak fitting, data analysis, machine learning

Date published: 2024-10-10

Publisher: Surface Science Society Japan

Journal:

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

Funding:

Manuscript type: Author's version (Accepted manuscript)

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

First published URL: https://doi.org/10.1380/vss.67.500

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Updated at: 2025-01-10 16:31:04 +0900

Published on MDR: 2025-01-10 16:31:04 +0900