论文标题
带有宽场的智能渗透方法，用于实时瞬态检测的小光圈望远镜
Smart obervation method with wide field small aperture telescopes for real time transient detection
论文作者
论文摘要
宽场小孔望远镜（WFSAT）通常用于快速天空调查。由几个WFSAT组成的望远镜阵列每晚能够几次扫描天空。他们将获得大量数据，这些数据需要立即处理。在本文中，我们提出了Argus（统一望远镜的天文目标检测框架）用于实时运输检测。 Argus使用基于深度学习的天文检测算法，该算法在每个WFSATS中的嵌入式设备中实现，以检测天文目标。检测是天文学目标的位置和概率将被发送到训练有素的集合学习算法，以输出天体来源的信息。在将这些源与Star目录匹配之后，Argus将直接输出瞬态候选者的类型和位置。我们使用模拟数据来测试Argus的性能，并发现Argus可以在瞬时检测任务中提高WFSAT的性能。
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.
