NEARBY Platform for Detecting Asteroids in Astronomical Images Using Cloud-based Containerized Applications
This addresses the need for more computational resources and algorithms to process large survey data for near real-time detection of near-Earth objects, but appears incremental as it builds on existing blink detection techniques.
The paper tackles the problem of detecting asteroids in astronomical images by introducing the NEARBY platform, which automates detection using classic blink methods and visual validation, though no concrete performance numbers are provided.
The continuing monitoring and surveying of the nearby space to detect Near Earth Objects (NEOs) and Near Earth Asteroids (NEAs) are essential because of the threats that this kind of objects impose on the future of our planet. We need more computational resources and advanced algorithms to deal with the exponential growth of the digital cameras' performances and to be able to process (in near real-time) data coming from large surveys. This paper presents a software platform called NEARBY that supports automated detection of moving sources (asteroids) among stars from astronomical images. The detection procedure is based on the classic "blink" detection and, after that, the system supports visual analysis techniques to validate the moving sources, assisted by static and dynamical presentations.