Mahlke, M., Solano, E., Bouy, H., Carry, B., Kleijn, G. A. V., Bertín, E. 2019. The ssos pipeline: Identification of Solar System objects in astronomical images. Astronomy and Computing 28, DOI: 10.1016/j.ascom.2019.100289
Observatories and satellites around the globe produce tremendous amounts of imaging data to study many different astrophysical phenomena. The serendipitous observations of Solar System objects are a fortunate by-product which have often been neglected due to the lack of a simple yet efficient identification algorithm. Meanwhile, the determination of the orbit, chemical composition, and physical properties such as rotation period and 3D-shape of Solar System objects requires a large number of astrometry and multi-band photometry observations. Such observations are hidden in current and future astrophysical archives, and a method to harvest these goldmines is needed.
This article presents an easy-to-implement, light-weight software package which detects bodies of the Solar System in astronomical images and measures their astrometry and photometry. The ssos pipeline is versatile, allowing for application to all kinds of observatory imaging products. The sole principle requirement is that the images observe overlapping areas of the sky within a reasonable time range. Both known and unknown Solar System objects are recovered, from fast-moving near-Earth asteroids to slow objects in the distant Kuiper belt.
The high-level pipeline design and two test applications are described here, highlighting the versatility of the algorithm with both narrow-field pointed and wide-field survey observations. In the first study, 2,828 detections of 204 SSOs are recovered from publicly available images of the GTC OSIRIS Broad Band DR1 (Cortes-Contreras, in preparation). The false-positive ratio of SSO detections ranges from 0% – 23% depending on the pipeline setup. The second test study utilizes the images of the first data release of J-PLUS, a 12-band optical survey. 4,606 SSO candidates are recovered, with a false-positive ratio of (2.0 +/- 0.2)%. A stricter pipeline parameter setup recovers 3,696 candidates with a sample contamination below 0.68%. (C) 2019 Elsevier B.V. All rights reserved.