K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy
| Autor: | Roland J. Koch, Thomas E. Beechem, Jeremy T. Robinson, Charles Melton, Xiaotian Zhang, Taisuke Ohta, Marcus M. Noack, Chris Jozwiak, Eli Rotenberg, Aaron Bostwick, Alexander Hexemer, Petrus H. Zwart |
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| Rok vydání: | 2020 |
| Předmět: |
Data collection
Computer science spectra k-means clustering Angle-resolved photoemission spectroscopy Human-Computer Interaction Set (abstract data type) symbols.namesake Data point machine learning Artificial Intelligence Metric (mathematics) symbols gaussian process Generic health relevance Cluster analysis Gaussian process Algorithm Software arpes |
| Zdroj: | Machine Learning: Science and Technology, vol 1, iss 4 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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