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The survey of the nearby space and continuous monitoring of the Near Earth Objects (NEOs) and especially Near Earth Asteroids (NEAs) are essential for the future of our planet and should represent a priority for our solar system research and nearby space exploration. More computing power and sophisticated digital tracking algorithms are needed to cope with the larger astronomy imaging cameras dedicated for survey telescopes. The paper presents the NEARBY platform that aims to experiment new algorithms for automatic image reduction, detection and validation of moving objects in astronomical surveys, specifically NEAs. The NEARBY platform has been developed and experimented through a collaborative research work between the Technical University of Cluj-Napoca (UTCN) and the University of Craiova, Romania, using observing infrastructure of the Instituto de Astrofisica de Canarias (IAC) and Isaac Newton Group (ING), La Palma, Spain. The NEARBY platform has been developed and deployed on the UTCNs cloud infrastructure and the acquired images are processed remotely by the astronomers who transfer it from ING through the web interface of the NEARBY platform. The paper analyzes and highlights the main aspects of the NEARBY platform development, and the results and conclusions on the EURONEAR surveys.
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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.
Near Earth Asteroids (NEAs) are discovered daily, mainly by few major surveys, nevertheless many of them remain unobserved for years, even decades. Even so, there is room for new discoveries, including those submitted by smaller projects and amateur astronomers. Besides the well-known surveys that have their own automated system of asteroid detection, there are only a few software solutions designed to help amateurs and mini-surveys in NEAs discovery. Some of these obtain their results based on the blink method in which a set of reduced images are shown one after another and the astronomer has to visually detect real moving objects in a series of images. This technique becomes harder with the increase in size of the CCD cameras. Aiming to replace manual detection we propose an automated pipeline prototype for asteroids detection, written in Python under Linux, which calls some 3rd party astrophysics libraries.
We propose a low-cost robotic optical survey aimed at $1-300$ m Near Earth Objects (NEO) based on four state-of-the-art telescopes having extremely wide field of view. The small Near-Earth Asteroids (NEA) represent a potential risk but also easily accessible space resources for future robotic or human space in-situ exploration, or commercial activities. The survey system will be optimized for the detection of fast moving - trailed - asteroids, space debris and will provide real-time alert notifications. The expected cost of the system including 1-year development and 2-year operation is 1,000,000 EUR. The successful demonstration of the system will promote cost-efficient ADAM-WFS (Automatic Detection of Asteroids and Meteoroids - A Wide Field Survey) systems to be built around the world.
Asteroids detection is a very important research field that received increased attention in the last couple of decades. Some major surveys have their own dedicated people, equipment and detection applications, so they are discovering Near Earth Asteroids (NEAs) daily. The interest in asteroids is not limited to those major surveys, it is shared by amateurs and mini-surveys too. A couple of them are using the few existent software solutions, most of which are developed by amateurs. The rest obtain their results in a visual manner: they blink a sequence of reduced images of the same field, taken at a specific time interval, and they try to detect a real moving object in the resulting animation. Such a technique becomes harder with the increase in size of the CCD cameras. Aiming to replace manual detection, we propose an automated blink technique for asteroids detection.
The world astronomical image archives represent huge opportunities to time-domain astronomy sciences and other hot topics such as space defense, and astronomical observatories should improve this wealth and make it more accessible in the big data era. In 2010 we introduced the Mega-Archive database and the Mega-Precovery server for data mining images containing Solar system bodies, with focus on near Earth asteroids (NEAs). This paper presents the improvements and introduces some new related data mining tools developed during the last five years. Currently, the Mega-Archive has indexed 15 million images available from six major collections (CADC, ESO, ING, LCOGT, NVO and SMOKA) and other instrument archives and surveys. This meta-data index collection is daily updated (since 2014) by a crawler which performs automated query of five major collections. Since 2016, these data mining tools run to the new dedicated EURONEAR server, and the database migrated to SQL engine which supports robust and fast queries. To constrain the area to search moving or fixed objects in images taken by large mosaic cameras, we built the graphical tools FindCCD and FindCCD for Fixed Objects which overlay the targets across one of seven mosaic cameras (Subaru-SuprimeCam, VST-OmegaCam, INT-WFC, VISTA-VIRCAM, CFHT-MegaCam, Blanco-DECam and Subaru-HSC), also plotting the uncertainty ellipse for poorly observed NEAs. In 2017 we improved Mega-Precovery, which offers now two options for calculus of the ephemerides and three options for the input (objects defined by designation, orbit or observations). Additionally, we developed Mega-Archive for Fixed Objects (MASFO) and Mega-Archive Search for Double Stars (MASDS). We believe that the huge potential of science imaging archives is still insufficiently exploited.
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