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DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning

Duev, Dmitry A. and Mahabal, Ashish and Ye, Quanzhi and Tirumala, Kushal and Belicki, Justin and Dekany, Richard and Frederick, Sara and Graham, Matthew J. and Laher, Russ R. and Masci, Frank J. and Prince, Thomas A. and Riddle, Reed and Rosnet, Philippe and Soumagnac, Maayane T. (2019) DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning. Monthly Notices of the Royal Astronomical Society, 486 (3). pp. 4158-4165. ISSN 0035-8711. doi:10.1093/mnras/stz1096.

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We present DeepStreaks, a convolutional-neural-network, deep-learning system designed to efficiently identify streaking fast-moving near-Earth objects that are detected in the data of the Zwicky Transient Facility (ZTF), a wide-field, time-domain survey using a dedicated 47 deg2 camera attached to the Samuel Oschin 48-inch Telescope at the Palomar Observatory in California, United States. The system demonstrates a 96-98% true positive rate, depending on the night, while keeping the false positive rate below 1%. The sensitivity of DeepStreaks is quantified by the performance on the test data sets as well as using known near-Earth objects observed by ZTF. The system is deployed and adapted for usage within the ZTF Solar-System framework and has significantly reduced human involvement in the streak identification process, from several hours to typically under 10 minutes per day.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper ItemDeepStreaks code and pre-trained models
Duev, Dmitry A.0000-0001-5060-8733
Mahabal, Ashish0000-0003-2242-0244
Ye, Quanzhi0000-0002-4838-7676
Frederick, Sara0000-0001-9676-730X
Graham, Matthew J.0000-0002-3168-0139
Laher, Russ R.0000-0003-2451-5482
Masci, Frank J.0000-0002-8532-9395
Prince, Thomas A.0000-0002-8850-3627
Riddle, Reed0000-0002-0387-370X
Soumagnac, Maayane T.0000-0001-6753-1488
Additional Information:© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model ( Accepted 2019 April 13. Received 2019 April 11; in original form 2019 February 25. D.A. Duev acknowledges support from the Heising-Simons Foundation under Grant No. 12540303. Q.-Z. Ye is supported by the GROWTH project funded by the National Science Foundation under Grant No. 1545949. Based on observations obtained with the Samuel Oschin Telescope 48-inch Telescope at the Palomar Observatory as part of the Zwicky Transient Facility project. Major funding has been provided by the U.S. National Science Foundation under Grant No. AST-1440341 and by the ZTF partner institutions: the California Institute of Technology, the Oskar Klein Centre, the Weizmann Institute of Science, the University of Maryland, the University of Washington, Deutsches Elektronen-Synchrotron, the University of Wisconsin-Milwaukee, and the TANGO Program of the University System of Taiwan. AM acknowledges support from NSF (1640818 and AST-1815034).
Group:Infrared Processing and Analysis Center (IPAC), Zwicky Transient Facility, Astronomy Department
Funding AgencyGrant Number
Heising-Simons Foundation12540303
ZTF partner institutionsUNSPECIFIED
Subject Keywords:methods: data analysis, asteroids: general, surveys
Issue or Number:3
Record Number:CaltechAUTHORS:20190501-152328124
Persistent URL:
Official Citation:Dmitry A Duev, Ashish Mahabal, Quanzhi Ye, Kushal Tirumala, Justin Belicki, Richard Dekany, Sara Frederick, Matthew J Graham, Russ R Laher, Frank J Masci, Thomas A Prince, Reed Riddle, Philippe Rosnet, Maayane T Soumagnac, DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning, Monthly Notices of the Royal Astronomical Society, Volume 486, Issue 3, July 2019, Pages 4158–4165,
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:95146
Deposited By: George Porter
Deposited On:01 May 2019 22:31
Last Modified:16 Nov 2021 17:10

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