Hubble Asteroid Hunter: I. Identifying asteroid trails in Hubble Space Telescope images
Sandor Kruk, Pablo García Martín, Marcel Popescu, Bruno Merín, Max Mahlke, Benoît Carry, Ross Thomson, Samet Karadag, Javier Durán, Elena Racero, Fabrizio Giord… 展开作者
Sandor Kruk, Pablo García Martín, Marcel Popescu, Bruno Merín, Max Mahlke, Benoît Carry, Ross Thomson, Samet Karadag, Javier Durán, Elena Racero, Fabrizio Giordano, Deborah Baines, Guido de Marchi, René Laureijs
Large and publicly available astronomical archives open up new possibilities to search and study Solar System objects. However, advanced techniques are required to deal with the large amounts of data. These unbiased surveys can be used to constrain the size distribution of minor bodies, which represents a piece of the puzzle for the formation models of the Solar System. We aim to identify asteroids in archival images from the ESA Hubble Space Telescope (HST) Science data archive using data mining. We developed a citizen science project on the Zooniverse platform, Hubble Asteroid Hunter (www.asteroidhunter.org) asking members of the public to identify asteroid trails in archival HST images. We used the labels provided by the volunteers to train an automated deep learning model built with Google Cloud AutoML Vision to explore the entire HST archive to detect asteroids crossing the field-of-view. We report the detection of 1701 new asteroid trails identified in archival HST data via our citizen science project and the subsequent machine learning exploration of the ESA HST science data archive. We detect asteroids to a magnitude of 24.5, which are statistically fainter than the populations of asteroids identified from ground-based surveys. The majority of asteroids are distributed near the ecliptic plane, as expected, where we find an approximate density of 80 asteroids per square degree. We match 670 trails (39% of the trails found) with 454 known Solar System objects in the Minor Planet Center database, however, no matches are found for 1031 (61%) trails. The unidentified asteroids are faint, being on average 1.6 magnitudes fainter than the asteroids we succeeded to identify. They probably correspond to previously unknown objects. This work demonstrates that citizen science and machine learning are useful techniques for the systematic search of SSOs in existing astronomy science archives.
Detection of asteroid trails in Hubble Space Telescope images using Deep Learning
Space Science Institute（空间科学研究所）；International Computer High School of Constanţa（康斯坦察国际计算机高中）；International Computer High School of Bucharest（布加勒斯特国际计算机高中）
JWST Observations of Asteroid 2024 YR4 Rule Out a 2032 Lunar Impact and Demonstrate a New Regime for Planetary Defense Follow-up
Massachusetts Institute of Technology（麻省理工学院）；Johns Hopkins University Applied Physics Laboratory（约翰斯·霍普金斯大学应用物理实验室）；European Space Agency, Near Earth Object Coordination Centre（欧洲空间局近地天体协调中心）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Space Telescope Science Institute（太空望远镜科学研究所）；Institute for Astronomy, University of Hawaii at Manoa（夏威夷大学马诺阿分校天文研究所）；Max-Planck-Institut für extraterrestrische Physik（马克斯·普朗克地外物理研究所）；Association of Universities for Research in Astronomy（大学天文研究协会）；NASA Goddard Space Flight Center（美国宇航局戈达德太空飞行中心）；Ondřejov Observatory（奥德杰约夫天文台）；Northern Arizona University（北亚利桑那大学）
