Interpretable Faraday Complexity Classification
  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.
