http://arxiv.org/abs/1911.08327
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.
D. Paranjpye, A. Mahabal, A. Ramaprakash, et. al.
Wed, 20 Nov 19
61/73
Comments: 7 pages, 15 figures
You must be logged in to post a comment.