Astrophysical transients with rapid development on sub-hour 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 followup. This paper presents the Evryscope Fast Transient Engine (EFTE), a new data reduction pipeline 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 suited to continuously monitoring the transient sky at minute cadence. Candidates are produced within the base Evryscope two-minute cadence for 98.5% of images, and internally filtered using VetNet, a convolutional neural network real-bogus classifier. EFTE provides an extensible, robust architecture for transient surveys probing similar timescales, and serves as the software testbed 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 62x higher than Evryscope.
@article{arxiv.2302.10929,
title = {The Evryscope Fast Transient Engine: Real-Time Detection for Rapidly Evolving Transients},
author = {Hank Corbett and Jonathan Carney and Ramses Gonzalez and Octavi Fors and Nathan Galliher and Amy Glazier and Ward S. Howard and Nicholas M. Law and Robert Quimby and Jeffrey K. Ratzloff and Alan Vasquez Soto},
journal= {arXiv preprint arXiv:2302.10929},
year = {2023}
}