Smart obervation method with wide field small aperture telescopes for real time transient detection
This work addresses the challenge of real-time transient detection for astronomers using WFSAT arrays, offering an incremental improvement in processing large datasets.
The paper proposes ARGUS, a framework for real-time transient detection using wide-field small-aperture telescopes (WFSATs). ARGUS employs deep learning on embedded devices within each WFSAT to identify astronomical targets, which are then processed by an ensemble learning algorithm and matched with star catalogs to output transient candidates. The system was tested with simulated data and demonstrated robust improvement in transient detection performance for WFSATs.
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.