Real-bogus classification for the Zwicky Transient Facility using deep learning

Dmitry A. Duev, Ashish Mahabal, Frank J. Masci, Matthew Graham, Ben Rusholme, Richard S. Walters, Ishani Karmarkar, Sarah Frederick, Mansi Kasliwal, Umaa Rebbapragada, Charlotte Ward

Abstract:

Efficient automated detection of flux-transient, re-occurring flux-variable, and moving objects is increasingly important for large-scale astronomical surveys. We present BRAAI, a
convolutional-neural-network, deep-learning real/bogus classifier designed to separate genuine
astrophysical events and objects from false positive, or bogus, detections in the data of the
Zwicky Transient Facility (ZTF), a new robotic time-domain survey currently in operation
at the Palomar Observatory in California, USA. BRAAI demonstrates a state-of-the-art
performance as quantified by its low false negative and false positive rates. We describe
the open-source software tools used internally at Caltech to archive and access ZTF’s alerts
and light curves (KOWALSKI), and to label the data (ZWICKYVERSE). We also report the initial
results of the classifier deployment on the Edge Tensor Processing Units that show comparable
performance in terms of accuracy, but in a much more (cost-) efficient manner, which has
significant implications for current and future surveys.