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We present DeepStreaks, a convolutional-neural-network, deep-learning systemdesigned to efficiently identify streaking fast-moving near-Earth objects thatare detected in the data of the Zwicky Transient Facility (ZTF), a wide-field,time-domain survey using a dedicated 47 sq. deg camera attached to the SamuelOschin 48-inch Telescope at the Palomar Observatory in California, UnitedStates. The system demonstrates a 96-98% true positive rate, depending on thenight, while keeping the false positive rate below 1%. The sensitivity ofDeepStreaks is quantified by the performance on the test data sets as well asusing known near-Earth objects observed by ZTF. The system is deployed andadapted for usage within the ZTF Solar-System framework and has significantlyreduced human involvement in the streak identification process, from severalhours to typically under 10 minutes per day.

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