K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy. Issue 4 (15th October 2020)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Machine learning: science and technology. Volume 1:Issue 4(2020)
• Publication Date: 2020-10-15
• Subjects: arpes -- gaussian process -- k-means clustering -- machine learning -- spectra; 006.31
• DOI: 10.1088/2632-2153/abab61
• Languages: English
• ISSNs: 2632-2153

Abstract

Abstract: 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 experimental phase diagram from as low as 12% of the original dataset size. In addition to the phase diagram, the GP is able to reconstruct spectra in energy-momentum space from this minimal set of data points. These findings suggest that this methodology can be used to improve the efficiency of ARPES data collection strategies for unknown samples. The practical feasibility of implementing this technology at a synchrotron beamline and the overall efficiency implications of this method are discussed with a view on enabling the collection of more samples or rapid identification of regions of interest.