Robust Automated Photometry Pipeline for Blurred Images
Robust Automated Photometry Pipeline for Blurred Images
复制标题
用于模糊图像的强大的自动光度测量管道
DOI：
10.1088/1538-3873/ab8e9b
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发表时间：
2020
影响因子：
3.5
通讯作者：
Wang Feng
中科院分区：
文献类型：
--
作者：
Huang Weirong;Xie Zhou;Zhong Wenjie;Mei Ying;Deng Hui;Liu Yingbo;Wang Feng
由国家天文台和广州大学联合运行的1.26米望远镜的主要任务是对g、r和i波段进行光度观测。采用IRAF、SExtractor、SCAMP等成熟软件包，建立了数据处理流水线系统，每天自动处理观测数据约5gb。但由于望远镜跟踪误差的存在，使得处理模糊图像的成功率明显降低；这反过来又极大地限制了望远镜的输出。我们提出了一个鲁棒的自动光度管道（RAPP）软件，可以正确地处理模糊图像。详细介绍了两项关键技术：模糊星增强和鲁棒图像匹配。一系列实验证明，RAPP不仅达到了与IRAF相当的光度成功率和精度，而且显著降低了数据处理负荷，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.