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

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

doi:10.1093/mnras/stz1096

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

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

California Institute of Technology
University of Maryland, College Park
Université Clermont Auvergne
Weizmann Institute of Science

Research output: Contribution to journal › Article › peer-review

41 Scopus citations

Abstract

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 per cent true positive rate, depending on the night, while keeping the false positive rate below 1 per cent. 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 min per day.

Original language	English
Pages (from-to)	4158-4165
Number of pages	8
Journal	Monthly Notices of the Royal Astronomical Society
Volume	486
Issue number	3
DOIs	https://doi.org/10.1093/mnras/stz1096
State	Published - 1 Jul 2019
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.

Funding

DAD acknowledges support from the Heising-Simons Foundation under Grant No. 12540303. QY is supported by the GROWTH project funded by the U.S. 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 Caltech, 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 the U.S. National Science Foundation (NSF) (1640818 and AST-1815034).

Funders	Funder number
U.S. National Science Foundation	AST-1440341
National Science Foundation	1440341, 1545949
American Committee for the Weizmann Institute of Science
University of Wisconsin-Milwaukee	1640818, AST-1815034
University of Washington
University of Maryland
Heising-Simons Foundation	12540303

Keywords

asteroids: general
methods: data analysis
minor planets
surveys

Access to Document

10.1093/mnras/stz1096

Cite this

Duev, D. A., Mahabal, A., Ye, Q., Tirumala, K., Belicki, J., Dekany, R., Frederick, S., Graham, M. J., Laher, R. R., Masci, F. J., Prince, T. A., Riddle, R., Rosnet, P., & Soumagnac, M. 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), 4158-4165. https://doi.org/10.1093/mnras/stz1096

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title = "DeepStreaks: Identifying fast-moving objects in the Zwicky Transient Facility data with deep learning",

abstract = "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 per cent true positive rate, depending on the night, while keeping the false positive rate below 1 per cent. 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 min per day.",

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doi = "10.1093/mnras/stz1096",

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Duev, DA, Mahabal, A, Ye, Q, Tirumala, K, Belicki, J, Dekany, R, Frederick, S, Graham, MJ, Laher, RR, Masci, FJ, Prince, TA, Riddle, R, Rosnet, P & Soumagnac, MT 2019, 'DeepStreaks: Identifying fast-moving objects in the Zwicky Transient Facility data with deep learning', Monthly Notices of the Royal Astronomical Society, vol. 486, no. 3, pp. 4158-4165. https://doi.org/10.1093/mnras/stz1096

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AU - Duev, Dmitry A.

AU - Mahabal, Ashish

AU - Ye, Quanzhi

AU - Tirumala, Kushal

AU - Belicki, Justin

AU - Dekany, Richard

AU - Frederick, Sara

AU - Graham, Matthew J.

AU - Laher, Russ R.

AU - Masci, Frank J.

AU - Prince, Thomas A.

AU - Riddle, Reed

AU - Rosnet, Philippe

AU - Soumagnac, Maayane T.

PY - 2019/7/1

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

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Bibliographical note

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Keywords

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