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.
Pan-STARRS Moving Object Processing System (MOPS) @cite_0 is a modern software package that produces automatic asteroid discoveries and identifications from catalogs of transient detections from next-generation astronomical survey telescopes. MOPS achieves 99:5 Besides major surveys, automated asteroid detection systems were also created by amateurs and small private surveys such as the TOTAS survey @cite_1 carried out with the ESA-OGS 1m telescope, lead by ESA, or the work of @cite_2 .
@cite_0 @cite_1 @cite_2
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ABSTRACT.We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern software package that produces automatic asteroid discoveries and identifications from catalogs of transient detections from next-generation astronomical survey telescopes. MOPS achieves >99.5 >99.5 efficiency in producing orbits from a synthetic but realistic population of asteroids whose measurements were simulated for a Pan-STARRS4-class telescope. Additionally, using a nonphysical grid population, we demonstrate that MOPS can detect populations of currently unknown objects such as interstellar asteroids. MOPS has been adapted successfully to the prototype Pan-STARRS1 telescope despite differences in expected false detection rates, fill-factor loss, and relatively sparse observing cadence compared to a hypothetical Pan-STARRS4 telescope and survey. MOPS remains highly efficient at detecting objects but drops to 80 efficiency at producing orbits. This loss is primarily due to configurable MOPS processing limits that a... Abstract ESA's 1-m telescope on Tenerife, the Optical Ground Station (OGS), has been used for observing NEOs since 2009. Part of the observational activity is the demonstration and test of survey observation strategies. During the observations, a total of 11 near-Earth objects have been discovered in about 360 h of observing time from 2009 to 2014. The survey observations are performed by imaging the same area in the sky 3 or 4 times within a 15–20 min time interval. A software robot analyses the images, searching for moving objects. The survey strategies and related data processing algorithms are described in this paper. In this work we present a system for autonomous discovery of asteroids, space trash and other moving objects. This system performs astronomical image data reduction based on an image processing pipeline. The processing steps of the pipeline include astrometric and photometric reduction, sequence alignment, moving object detection and astronomical analysis, making the system capable of discovering and monitoring previously unknown moving objects in the night sky.