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A Robust Principal Component Analysis for Outlier Identification in Messy Microcalorimeter Data



Joseph W. Fowler, Bradley K. Alpert, Young I. Joe, Galen C. O'Neil, Daniel S. Swetz, Joel N. Ullom


A principal component analysis (PCA) of clean microcalorimeter pulse records can be a first step beyond statistically optimal linear filtering of pulses toward a fully nonlinear analysis. For PCA to be practical on spectrometers with hundreds of sensors, an automated identification of clean pulses is required. Robust forms of PCA are the subject of active research in machine learning. We examine a version known as coherence pursuit that is simple and fast and well matched to the automatic identification of outlier records, as needed for microcalorimeter pulse analysis.
Journal of Low Temperature Physics


outlier identification, microcalorimeter, principal component analysis, PCA, pulse records, spectrometers, clean pulses, machine learning
Created November 11, 2019, Updated July 6, 2020