Enabling discovery of solar system objects in large alert data streams

Abstract

With the advent of large-scale astronomical surveys such as the Zwicky Transient Facility (ZTF), the number of alerts generated by transient, variable and moving astronomical objects is growing rapidly, reaching millions per night. Concerning solar system minor planets, their identification requires linking the alerts of many observations over a potentially large time, leading to a very large combinatorial number. This work aims to identify new candidates for solar system objects from massive alert data streams produced by large-scale surveys, such as the ZTF and the Vera C. Rubin Observatory's Legacy Survey of Space and Time. Our analysis used the Fink alert broker capabilities to reduce the 111,275,131 processed alerts from ZTF between November 2019 and December 2022 to only 389,530 new solar system alert candidates over the same period. We then implemented a linking algorithm, Fink-FAT, to create real-time trajectory candidates from alert data and extract orbital parameters. The analysis was validated on ZTF alert packets linked to confirmed solar system objects from the Minor Planet Center database. Finally, the results were confronted against follow-up observations. Between November 2019 and December 2022, Fink-FAT extracted 327 new orbits from solar system object candidates at the time of the observations, over which 65 were still unreported in the MPC database as of March 2023. After two late follow-up observation campaigns of six orbit candidates, four were associated with known solar system minor planets, and two remain unknown. Fink-FAT is deployed in the Fink broker and successfully analyzes in real time the alert data from the ZTF survey by regularly extracting new candidates for solar system objects. Our scalability tests also show that Fink-FAT can handle the even larger volume of alert data that the Rubin Observatory will send.

arXiv commentPublished in A&A

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• The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection