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

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.

Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning

“…For example, telescopes are assumed as ideal detectors, which does not consider observation environments or abilities of these telescopes. Besides, these observation strategies do not consider real locations of telescopes and distributions of celestial objects, which would reduce their effectiveness (Jia et al 2020(Jia et al , 2022b.…”