Abstract
Abstract We present Tails, an open-source deep-learning framework for the identification and localization of comets in the image data of the Zwicky Transient Facility (ZTF), a robotic optical time-domain survey currently in operation at the Palomar Observatory in California, USA. Tails employs a custom EfficientDet-based architecture and is capable of finding comets in single images in near real time, rather than requiring multiple epochs as with traditional methods. The system achieves state-of-the-art performance with 99% recall, a 0.01% false-positive rate, and a 1–2 pixel rms error in the predicted position. We report the initial results of the Tails efficiency evaluation in a production setting on the data of the ZTF Twilight survey, including the first AI-assisted discovery of a comet (C/2020 T2) and the recovery of a comet (P/2016 J3 = P/2021 A3).
Highlights
Comets have mesmerized humans for millennia, frequently offering, arguably, some of the most spectacular sights in the night sky
It is a very exciting time to look for comets: the large-scale time-domain surveys that are currently in operation, such as the Zwicky Transient Facility (ZTF; Bellm et al 2019a; Graham et al 2019), Panoramic Survey Telescope & Rapid Response System (Pan-STARRS; Chambers et al 2016), or Asteroid Terrestrial-impact Last Alert System (ATLAS; Tonry et al 2018), and the upcoming ones such as BlackGEM (Bloemen et al 2016) and Vera Rubin Observatory/Large Synoptic Survey Telescope (LSST; Ivezić et al 2008) offer the richest data sets ever available to mine for comets
ZTF observes the sky in the g, r, and i bands at different cadences depending on the scientific program and sky region (Bellm et al 2019a; Graham et al 2019)
Summary
Comets have mesmerized humans for millennia, frequently offering, arguably, some of the most spectacular sights in the night sky. The recent discovery of the first interstellar comet 2I/Borisov by amateur astronomer Gennadiy Borisov predictably sparked much excitement and enthusiasm among astronomers and the general public alike (e.g., Bolin et al 2020; Fitzsimmons et al 2019; Guzik et al 2020). Such objects could potentially provide important information on the formation of other stellar systems. Traditional comet detection algorithms rely on multiple observations of cometary objects that are linked together and used to fit an orbital solution. We present Tails—a state-of-the-art open-source deep-learning-based system for the identification and localization of comets in the image data of ZTF. Tails employs an EfficientDet-based architecture (Tan et al 2019) and is capable of finding comets in single images in near real time, rather than requiring multiple epochs as with traditional methods
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