Polarization measurements done using Imaging Polarimeters such as the Robotic Polarimeter are very sensitive to the presence of artefacts in images. Artefacts can range from internal reflections in a telescope to satellite trails that could contaminate an area of interest in the image. With the advent of wide-field polarimetry surveys, it is imperative to develop methods that automatically flag artefacts in images. In this paper, we implement a Convolutional Neural Network to identify the most dominant artefacts in the images. We find that our model can successfully classify sources with 98\% true positive and 97\% true negative rates. Such models, combined with transfer learning, will give us a running start in artefact elimination for near-future surveys like WALOP.
@article{arxiv.1911.08327,
title = {Eliminating artefacts in Polarimetric Images using Deep Learning},
author = {Dhruv Paranjpye and Ashish Mahabal and A. N. Ramaprakash and Gina Panopoulou and Kieran Cleary and Anthony Readhead and Dmitry Blinov and Kostas Tassis},
journal= {arXiv preprint arXiv:1911.08327},
year = {2019}
}