Towards Efficient Detection of Small Near-Earth Asteroids Using the Zwicky Transient Facility (ZTF)

• Published in: arXiv.org 2019-04
• Main Authors: Ye, Quanzhi, Masci, Frank J, Hsing Wen Lin, Bolin, Bryce, Chan-Kao, Chang, Duev, Dmitry A, Helou, George, Ip, Wing-Huen, Kaplan, David L, Kramer, Emily, Mahabal, Ashish, Chow-Choong Ngeow, Nielsen, Avery J, Prince, Thomas A, Tan, Hanjie, Yeh, Ting-Shuo, Bellm, Eric C, Dekany, Richard, Giomi, Matteo, Graham, Matthew J, Kulkarni, Shrinivas R, Kupfer, Thomas, Laher, Russ R, Rusholme, Ben, Shupe, David L, Ward, Charlotte
• Format: Article
• Language: English
• Subjects: Asteroid detection; Asteroids; Machine learning; Near-Earth Objects; Object recognition; Real time
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1904.09645

We describe ZStreak, a semi-real-time pipeline specialized in detecting small, fast-moving near-Earth asteroids (NEAs) that is currently operating on the data from the newly-commissioned Zwicky Transient Facility (ZTF) survey. Based on a prototype originally developed by Waszczak et al. (2017) for the Palomar Transient Factory (PTF), the predecessor of ZTF, ZStreak features an improved machine-learning model that can cope with the \(10\times\) data rate increment between PTF and ZTF. Since its first discovery on 2018 February 5 (2018 CL), ZTF/ZStreak has discovered \(45\) confirmed new NEAs over a total of 232 observable nights until 2018 December 31. Most of the discoveries are small NEAs, with diameters less than \(\sim100\) m. By analyzing the discovery circumstances, we find that objects having the first to last detection time interval under 2 hr are at risk of being lost. We will further improve real-time follow-up capabilities, and work on suppressing false positives using deep learning.