Tails: Chasing Comets with the Zwicky Transient Facility and Deep Learning

Dmitry A. Duev, Bryce T. Bolin, Matthew J. Graham, Michael S.P. Kelley, Ashish Mahabal, Eric C. Bellm, Michael W. Coughlin, Richard Dekany, George Helou, Shrinivas R. Kulkarni, Frank J. Masci, Thomas A. Prince, Reed Riddle, Maayane T. Soumagnac, Stéfan J. Van Der Walt

Research output: Contribution to journalArticlepeer-review

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).

Original languageEnglish (US)
Article number218
JournalAstronomical Journal
Volume161
Issue number5
DOIs
StatePublished - May 2021

Bibliographical note

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© 2021. The American Astronomical Society. All rights reserved.

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