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.

中文翻译：

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K均值驱动的高斯过程数据收集，用于角度分辨光发射光谱

我们建议将k均值聚类与高斯过程（GP）回归相结合，以分析和探索4D角分辨光发射光谱（ARPES）数据。使用群集标签作为在其上训练GP的驱动指标，该方法使我们能够从低至原始数据集大小的12％重构实验相图。除了相位图，GP还可以从这组最小的数据点重建能量动量空间中的光谱。这些发现表明，该方法可用于提高未知样品的ARPES数据收集策略的效率。