Abstract
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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Notes
Outlier pursuit thus relies on the Anna Karenina principle: Clean pulse records are all alike; every unclean record is unclean in its own way.
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Acknowledgements
This work was supported by NIST’s Innovations in Measurement Science program and by NASA SAT NNG16PT18I, “Enabling & enhancing technologies for a demonstration model of the Athena X-IFU.” We thank Dan Becker and Malcolm Durkin for numerous discussions and earlier work on pulse outlier identification.
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Fowler, J.W., Alpert, B.K., Joe, YI. et al. A Robust Principal Component Analysis for Outlier Identification in Messy Microcalorimeter Data. J Low Temp Phys 199, 745–753 (2020). https://doi.org/10.1007/s10909-019-02248-w
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DOI: https://doi.org/10.1007/s10909-019-02248-w