Mega-Archive and the EURONEAR tools for data mining world astronomical images

Abstract

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. In this sense, defining and making available a standard format for indexing meta-data needed to access the image archives could strongly enhance their use. We recommend to IAU to define such a standard and ask the astronomical observatories to adopt it for indexing their image archives in a homogeneous manner, and make these indexes available up to date, free of any proprietorship period.

Определение членства и базовых параметров рассеянных звёздных скоплений на основе Big Data Gaia EDR3; рассеянное скопление Praesepe

“…Стремительное и мощное развитие инструментальной базы астрономических исследований, связанное, в первую очередь, с введением в строй и эффективной эксплуатацией современных крупных наземных адаптивных телескопов и многофункциональных космических телескопов привело к формированию сверхбольших (петабайтных) баз астрономических данных VLDB (Very Large Data Base), относящихся к классу Big Data, и возникновению принципиально нового метода изучения астрофизических объектов, получившего название Data Mining (см., н-р., [1][2][3][4][5][6][7]). Это, в свою очередь, потребовало разработки принципиально новых методов их обработки и анализа с применением элементов искусственного интеллекта (нейронные сети, распределённые (облачные) вычисления, машинное обучение разного уровня и т.п.)…”

Dozens of virtual impactor orbits eliminated by the EURONEAR VIMP DECam data mining project

“…Our second project, Mega-Precovery, and few related tools have gathered 112 instrument archives in the Mega-Archive Structured Query Language (SQL) database, a meta-data collection holding more than 16 million images able to search immediately for one given known or unknown moving (Solar System) object or any fixed (stellar or extra-galactic) object (Vaduvescu et al 2020). This big data mining resource is similar to the Canadian Solar System Object Image Search (SSOIS) 2 .…”