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 %CONTENT%gt;$10% of the FWHM of the Faraday Point Spread Function, the polarized flux ratio of the sources is %CONTENT%gt;$0.1, and that the Signal-to-Noise radio (S/N) of the primary component is %CONTENT%gt;$5. With this S/N cut-off, the false positive rate (simple sources mis-classified as complex) is %CONTENT%lt;$0.3%. Work is ongoing to include Faraday thick sources in the training and testing of the CNN.
Publisher URL: http://arxiv.org/abs/1711.03252
DOI: arXiv:1711.03252v1
Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.
Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.