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

J W Fowler, B K Alpert, Y-I Joe, G C O'Neil, D S Swetz, J N Ullom

Abstract read

In one paragraph

Article in Journal of low temperature physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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0 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

J W FowlerQuantum Sensors Group, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA.ORCID 0000-0002-8079-0895

B K AlpertQuantum Sensors Group, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA.

Y-I JoeQuantum Sensors Group, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA.

G C O'NeilQuantum Sensors Group, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA.

D S SwetzQuantum Sensors Group, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA.

J N UllomQuantum Sensors Group, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA.

Funding

Intramural NIST DOC 9999-NIST

6 · The paper itself

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.

Indexed as

MicrocalorimetersPulse analysisX-ray pulses

Identifiers

PMID33364637

DOI10.1007/s10909-019-02248-w

PMCPMC7754256

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.