Smart obervation method with wide field small aperture telescopes for real time transient detection

TL;DR

This paper introduces ARGUS, a real-time detection framework using deep learning and ensemble methods for wide field small aperture telescopes, enhancing transient detection capabilities in sky surveys.

Contribution

The paper presents a novel integrated system combining deep learning and ensemble algorithms for real-time astronomical transient detection with WFSATs.

Findings

ARGUS improves transient detection performance in simulated tests.

The framework enables immediate processing and identification of celestial sources.

It facilitates real-time sky survey data analysis with embedded devices.

Abstract

Wide field small aperture telescopes (WFSATs) are commonly used for fast sky survey. Telescope arrays composed by several WFSATs are capable to scan sky several times per night. Huge amount of data would be obtained by them and these data need to be processed immediately. In this paper, we propose ARGUS (Astronomical taRGets detection framework for Unified telescopes) for real-time transit detection. The ARGUS uses a deep learning based astronomical detection algorithm implemented in embedded devices in each WFSATs to detect astronomical targets. The position and probability of a detection being an astronomical targets will be sent to a trained ensemble learning algorithm to output information of celestial sources. After matching these sources with star catalog, ARGUS will directly output type and positions of transient candidates. We use simulated data to test the performance of ARGUS and find that ARGUS can increase the performance of WFSATs in transient detection tasks robustly.