Interpretable Faraday Complexity Classification

TL;DR

This paper presents interpretable machine learning features and classifiers to efficiently determine Faraday complexity in spectropolarimetric data, enabling rapid analysis of large radio surveys with high accuracy.

Contribution

It introduces five novel features for estimating Faraday complexity and demonstrates their effectiveness with simple, interpretable classifiers on both simulated and real data.

Findings

Achieved 95% accuracy on simulated ASKAP data.

Achieved 90% accuracy on simulated ATCA data.

First application of machine learning to real spectropolarimetry data.

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 per cent accuracy on simulated ASKAP data and 90 per cent 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.