2019/11/01 by J. W. Fowler, B. K. Alpert, Y.-I. Joe +5
Computer Science · Engineering · Physics and Astronomy · #Advanced SAR Imaging Techniques #Coherence (philosophical gambling strategy) #Identification (biology) #Nonlinear system #Outlier #Pattern recognition (psychology) #Principal component analysis #Robust principal component analysis #Robust statistics #Statistical Mechanics and Entropy #Wireless Signal Modulation Classification #physics.data-an #physics.ins-det
paper · pdf · doi:10.1007/s10909-019-02248-w
Accepted in J. Low Temperature Physics
arxiv created 2019/11/01 · openalex publication_date 2019/11/12 · openalex created_date 2019/11/22 · arxiv updated 2020/01/08 · openalex updated_date 2026/08/05
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.