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A principal component analysis (PCA) of clean microcalorimeter pulse records can be a first step beyond statistically optimal linear filtering of pulses towards a fully non-linear 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, fast, and well matched to the automatic identification of outlier records, as needed for microcalorimeter pulse analysis.
CUPID-Mo is a cryogenic detector array designed to search for neutrinoless double-beta decay ($0 ubetabeta$) of $^{100}$Mo. It uses 20 scintillating $^{100}$Mo-enriched Li$_2$MoO$_4$ bolometers instrumented with Ge light detectors to perform active s
For experiments with high arrival rates, reliable identification of nearly-coincident events can be crucial. For calorimetric measurements to directly measure the neutrino mass such as HOLMES, unidentified pulse pile-ups are expected to be a leading
The overall design of the Integrated Spectral Analysis Workbench (ISAW), being developed at Argonne, provides for an extensible, highly interactive, collaborating set of viewers for neutron scattering data. Large arbitrary collections of spectra from
Functional principal component analysis is essential in functional data analysis, but the inferences will become unconvincing when some non-Gaussian characteristics occur, such as heavy tail and skewness. The focus of this paper is to develop a robus
Fan et al. [$mathit{Annals}$ $mathit{of}$ $mathit{Statistics}$ $textbf{47}$(6) (2019) 3009-3031] proposed a distributed principal component analysis (PCA) algorithm to significantly reduce the communication cost between multiple servers. In this pape