Deep Sky Objects Detection with Deep Learning for Electronically Assisted Astronomy

Abstract

Electronically Assisted Astronomy is a fascinating activity requiring suitable conditions and expertise to be fully appreciated. Complex equipment, light pollution around urban areas and lack of contextual information often prevents newcomers from making the most of their observations, restricting the field to a niche expert audience. With recent smart telescopes, amateur and professional astronomers can capture efficiently a large number of images. However, post-hoc verification is still necessary to check whether deep sky objects are visible in the produced images, depending on their magnitude and observation conditions. If this detection can be performed during data acquisition, it would be possible to configure the capture time more precisely. While state-of-the-art works are focused on detection techniques for large surveys produced by professional ground-based observatories, we propose in this paper several Deep Learning approaches to detect celestial targets in images captured with smart telescopes, with a F1-score between 0.4 and 0.62 on test data, and we experimented them during outreach sessions with public in Luxembourg Greater Region.