Classifying Complex Faraday Spectra with Convolutional Neural Networks
  Advances in radio spectro-polarimetry offer the possibility to disentangle
complex regions where relativistic and thermal plasmas mix in the interstellar
and intergalactic media. Recent work has shown that apparently simple Faraday
Rotation Measure (RM) spectra can be generated by complex sources. This is true
even when the distribution of RMs in the complex source greatly exceeds the
errors associated with a single component fit to the peak of the Faraday
spectrum. We present a convolutional neural network (CNN) that can
differentiate between simple Faraday thin spectra and those that contain
multiple or Faraday thick sources. We demonstrate that this CNN, trained for
the upcoming Polarisation Sky Survey of the Universe's Magnetism (POSSUM) early
science observations, can identify two component sources 99% of the time,
provided that the sources are separated in Faraday depth by $>$10% of the FWHM
of the Faraday Point Spread Function, the polarized flux ratio of the sources
is $>$0.1, and that the Signal-to-Noise radio (S/N) of the primary component is
$>$5. With this S/N cut-off, the false positive rate (simple sources
mis-classified as complex) is $<$0.3%. Work is ongoing to include Faraday thick
sources in the training and testing of the CNN.
