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

TL;DR

The Evryscope Fast Transient Engine (EFTE) is a real-time data pipeline that detects rapidly evolving astrophysical transients at minute cadence, enabling timely follow-up observations and long-term light curve analysis.

Contribution

EFTE introduces a low-latency, robust pipeline utilizing direct image subtraction and neural network filtering for real-time transient detection with the Evryscope telescopes.

Findings

Candidates produced within two-minute cadence for 98.5% of images

Effective filtering with VetNet neural network

Framework adaptable for next-generation all-sky observatories

Abstract

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.