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, George Helou, Russ R. Laher, Frank J. Masci, Thomas A. Prince, Reed Riddle, Philippe Rosnet, Maayane T. Soumagnac
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 sq. deg 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.