Eliminating artefacts in polarimetric images using deep learning

D. Paranjpye, A. Mahabal, A. N. Ramaprakash, G.V. Panopoulou, K. Cleary, A. C. S. Readhead, D. Blinov, K. Tassis

Результат исследований: Научные публикации в периодических изданияхстатья


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 per cent true positive and 97 per cent true negative rates. Such models, combined with transfer learning, will give us a running start in artefact elimination for near-future surveys like WALOP.

Язык оригиналаАнглийский
Страницы (с-по)5151-5157
ЖурналMonthly Notices of the Royal Astronomical Society
Номер выпуска4
Ранняя дата в режиме онлайн28 ноя 2019
СостояниеОпубликовано - фев 2020
Опубликовано для внешнего пользованияДа


Paranjpye, D., Mahabal, A., Ramaprakash, A. N., Panopoulou, G. V., Cleary, K., Readhead, A. C. S., Blinov, D., & Tassis, K. (2020). Eliminating artefacts in polarimetric images using deep learning. Monthly Notices of the Royal Astronomical Society, 491(4), 5151-5157. https://doi.org/10.1093/mnras/stz3250