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A principal component analysis (PCA) of clean microcalorimeter pulse recordscan be a first step beyond statistically optimal linear filtering of pulsestowards a fully non-linear analysis. For PCA to be practical on spectrometerswith hundreds of sensors, an automated identification of clean pulses isrequired. Robust forms of PCA are the subject of active research in machinelearning. We examine a version known as coherence pursuit that is simple, fast,and well matched to the automatic identification of outlier records, as neededfor microcalorimeter pulse analysis.

A robust principal component analysis for outlier identification in messy microcalorimeter data