K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy

We propose the combination of k-means clustering with Gaussian Process (GP) regression in the analysis and exploration of 4D angle-resolved photoemission spectroscopy (ARPES) data. Using cluster labels as the driving metric on which the GP is trained, this method allows us to reconstruct the experim...

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Veröffentlicht in:	Machine learning: science and technology 2020-12, Vol.1 (4), p.45015
Hauptverfasser:	Melton, Charles N, Noack, Marcus M, Ohta, Taisuke, Beechem, Thomas E, Robinson, Jeremy, Zhang, Xiaotian, Bostwick, Aaron, Jozwiak, Chris, Koch, Roland J, Zwart, Petrus H, Hexemer, Alexander, Rotenberg, Eli
Format:	Artikel
Sprache:	eng
Schlagworte:	ARPES Gaussian Process INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY K-means clustering Machine Learning MATHEMATICS AND COMPUTING Spectra
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