The Evryscope Fast Transient Engine: Real-time Detection for Rapidly Evolving Transients

Abstract

Astrophysical transients with rapid developments on subhour timescales are intrinsically rare. Due to their short durations, events like stellar superflares, optical flashes from gamma-ray bursts, and shock breakouts from young supernovae are difficult to identify on timescales that enable spectroscopic follow-up. This paper presents the Evryscope Fast Transient Engine ( EFTE ), a new data reduction pipeline that is designed to provide low-latency transient alerts from the Evryscopes—a north–south pair of ultra-wide-field telescopes with an instantaneous footprint covering 38% of the entire sky—and tools for building long-term light curves from Evryscope data. EFTE leverages the optical stability of the Evryscopes by using a simple direct image subtraction routine that is suited to continuously monitoring the transient sky at a cadence of a minute. Candidates are produced within the base Evryscope 2 minute cadence for 98.5% of images, and internally filtered using vetnet , a convolutional neural network real–bogus classifier. EFTE provides an extensible and robust architecture for transient surveys probing similar timescales, and serves as the software test bed for the real-time analysis pipelines and public data distribution systems for the Argus Array, a next-generation all-sky observatory with a data rate 62 times higher than that of Evryscope.

Keywords

• Sky surveys
• Transient detection
• Stellar flares
• Convolutional neural networks
• Astronomy data reduction
• Astronomy image processing