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

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 i...

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Bibliographic Details
Published inJournal of low temperature physics Vol. 199; no. 3-4; pp. 745 - 753
Main Authors Fowler, J. W., Alpert, B. K., Joe, Y.-I., O’Neil, G. C., Swetz, D. S., Ullom, J. N.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.05.2020
Springer Nature B.V
Subjects
Characterization and Evaluation of Materials
Condensed Matter Physics
Data analysis
Low temperature physics
Machine learning
Magnetic Materials
Magnetism
Nonlinear analysis
Outliers (statistics)
Physics
Physics and Astronomy
Principal components analysis
Spectrometers
Microcalorimeters
Pulse analysis
X-ray pulses
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ISSN0022-2291
1573-7357
DOI10.1007/s10909-019-02248-w

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Summary: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.
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ISSN:0022-2291
1573-7357
DOI:10.1007/s10909-019-02248-w