论文标题
强大的自动化光度管道的模糊图像
Robust Automated Photometry Pipeline for Blurred Images
论文作者
论文摘要
由国家天文天文台和广州大学共同操作的1.26-m望远镜的主要任务是G，R和I乐队的光度观测值。使用成熟的软件包（例如IRAF，Sextractor和Scamp）建立了一个数据处理管道系统，每天自动处理大约5 GB的观察数据。但是，由于望远镜跟踪误差，处理模糊的图像时，成功率显着降低。反过来，这显着限制了望远镜的输出。我们提出了一个可以正确处理模糊图像的强大自动化学管道（RAPP）软件。详细介绍了两种关键技术：恒星增强和稳健的图像匹配。一系列测试证明，RAPP不仅取得了与IRAF相当的光度成功率和精度，而且还显着降低了数据处理负载并提高了效率。
The primary task of the 1.26-m telescope jointly operated by the National Astronomical Observatory and Guangzhou University is photometric observations of the g, r, and i bands. A data processing pipeline system was set up with mature software packages, such as IRAF, SExtractor, and SCAMP, to process approximately 5 GB of observational data automatically every day. However, the success ratio was significantly reduced when processing blurred images owing to telescope tracking error; this, in turn, significantly constrained the output of the telescope. We propose a robust automated photometric pipeline (RAPP) software that can correctly process blurred images. Two key techniques are presented in detail: blurred star enhancement and robust image matching. A series of tests proved that RAPP not only achieves a photometric success ratio and precision comparable to those of IRAF but also significantly reduces the data processing load and improves the efficiency.
