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arXiv 0907.1330astro-ph.IMastro-ph.HE
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SVOM可见光望远镜星载快速提取算法的优化

A Fast Onboard Star-Extraction Algorithm Optimized for the SVOM Visible Telescope

T. Wang, Y. Qiu, H. Cai, J. Deng

摘要:

空间多波段可变目标监测器(SVOM)是中国提出的一颗天文卫星,致力于在宇宙尺度上探测、定位和测量伽马射线暴(GRB)。针对SVOM星载可见光望远镜(VT)获取的CCD图像,提出了一种星载恒星提取的有效算法。参考星的CCD像素坐标将用于精确卫星的天文位置,这将有助于触发对伽马射线暴的快速地面跟踪观测。在该算法中,图像被划分为许多网格单元,每个单元内的“全局”像素值最大值被用作“亮”星的第一猜测位置。然后用一个简单的迭代方法计算出恒星的正确中心位置。应用两个额外的策略,即,提出了仅通过偶数(或奇数)行或以黑白棋盘模式扫描图像以进一步减少提取星星的时间。为了检验上述算法的有效性,我们将其应用于地面望远镜获得的实验图像。我们发现,我们的方法所实现的天文定位的精度是可比的,通过使用传统的恒星提取方法,而前者需要的CPU时间约25倍,比后者。这将大大提高SVOM VT任务的性能。

英文摘要:

The Space multi-band Variable Object Monitor (SVOM) is a proposed Chinese astronomical satellite, dedicated to the detection, localization and measurement of the gamma-ray bursts (GRBs) on the cosmological scale. An efficient algorithm is developed for the purpose of onboard star extraction from the CCD images obtained with the Visible Telescope (VT) on board SVOM. The CCD pixel coordinates of the reference stars will be used to refine the astronomical position of the satellite, which will facilitate trigger rapid ground-based follow-up obsevations of the GRBs. In this algorithm, the image is divided into a number of grid cells and the "global" pixel-value maximum within each cell is taken as the first-guess position of a "bright" star. The correct center postion of a star is then computed using a simple iterative method. Applying two additional strategies, i.e., scanning the image only by even (or odd) lines or in a black-white chess board mode, are proposed to further reduce the time to extract the stars. To examine the efficiency of the above aglorithms, we applied them to the experimental images obtained with a ground-based telescope. We find that the accuracy of the astronomical positioning achieved by our method is comparable to that derived by using the conventional star-extraction method, while the former needs CPU time about 25 times less than the latter. This will significantly improve the performance of the SVOM VT mission.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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