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Interpretable Faraday complexity classification

Published online by Cambridge University Press:  23 April 2021

M. J. Alger*
Affiliation:
Research School of Astronomy and Astrophysics, The Australian National University, Canberra, ACT 2611, Australia Data61, CSIRO, Canberra, ACT 2601, Australia
J. D. Livingston
Affiliation:
Research School of Astronomy and Astrophysics, The Australian National University, Canberra, ACT 2611, Australia
N. M. McClure-Griffiths
Affiliation:
Research School of Astronomy and Astrophysics, The Australian National University, Canberra, ACT 2611, Australia
O. I. Wong
Affiliation:
CSIRO Astronomy & Space Science, PO Box 1130, Bentley, WA 6102, Australia ICRAR-M468, University of Western Australia, Crawley, WA 6009, Australia ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia
C. S. Ong
Affiliation:
Data61, CSIRO, Canberra, ACT 2601, Australia Research School of Computer Science, The Australian National University, Canberra, ACT 2601, Australia
*
Author for correspondence: M. J. Alger, E-mail: matthew.alger@gmail.com

Abstract

Faraday complexity describes whether a spectropolarimetric observation has simple or complex magnetic structure. Quickly determining the Faraday complexity of a spectropolarimetric observation is important for processing large, polarised radio surveys. Finding simple sources lets us build rotation measure grids, and finding complex sources lets us follow these sources up with slower analysis techniques or further observations. We introduce five features that can be used to train simple, interpretable machine learning classifiers for estimating Faraday complexity. We train logistic regression and extreme gradient boosted tree classifiers on simulated polarised spectra using our features, analyse their behaviour, and demonstrate our features are effective for both simulated and real data. This is the first application of machine learning methods to real spectropolarimetry data. With 95% accuracy on simulated ASKAP data and 90% accuracy on simulated ATCA data, our method performs comparably to state-of-the-art convolutional neural networks while being simpler and easier to interpret. Logistic regression trained with our features behaves sensibly on real data and its outputs are useful for sorting polarised sources by apparent Faraday complexity.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Astronomical Society of Australia

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