一种从良好采样图像获得鲁棒高精度时间序列Photometry的新方法：应用于疏散星团M37的存档MMT/Megacam观测
A New Method For Robust High-Precision Time-Series Photometry From Well-Sampled Images: Application to Archival MMT/Megacam Observations of the Open Cluster M37
摘要：
我们介绍了强大的高精度测光从良好的采样图像的非拥挤的领域与强烈变化的点扩散函数的新方法。在这项工作中，我们使用了MMT 6.5米望远镜拍摄的疏散星团M37的档案成像数据。我们发现，档案光变曲线从原来的图像减法程序表现出许多不寻常的离群值，超过20%的数据得到拒绝通过简单的过滤算法采用早期分析。为了实现更好的测光精度，并利用所有可用的数据，整个成像数据库重新分析与我们的时间序列测光技术（多孔径索引测光）和一套复杂的校准程序。这种方法的优点如下：我们找到一个最佳的光圈为每一个最大的信噪比的恒星，也治疗特殊的情况下，测光返回误导信息更优的光度指数。我们还采用光度去趋势的基础上的层次聚类方法，这是一个非常有用的工具，在消除系统的光变曲线。我们的方法去除了附近恒星的光变曲线所共有的系统变化，同时保留了真实的变化。因此，我们的方法利用了近100%的可用数据，并将RMS散射降低到比较亮恒星的光变曲线小几倍。这个新的数据集提供了一个难得的机会，探索不同类型的变化，短（~分钟）和长（~1个月）的时间尺度在疏散星团恒星。
英文摘要：
We introduce new methods for robust high-precision photometry from well-sampled images of a non-crowded field with a strongly varying point-spread function. For this work, we used archival imaging data of the open cluster M37 taken by MMT 6.5m telescope. We find that the archival light curves from the original image subtraction procedure exhibit many unusual outliers, and more than 20% of data get rejected by the simple filtering algorithm adopted by early analysis. In order to achieve better photometric precisions and also to utilize all available data, the entire imaging database was re-analyzed with our time-series photometry technique (Multi-aperture Indexing Photometry) and a set of sophisticated calibration procedures. The merit of this approach is as follows: we find an optimal aperture for each star with a maximum signal-to-noise ratio, and also treat peculiar situations where photometry returns misleading information with more optimal photometric index. We also adopt photometric de-trending based on a hierarchical clustering method, which is a very useful tool in removing systematics from light curves. Our method removes systematic variations that are shared by light curves of nearby stars, while true variabilities are preserved. Consequently, our method utilizes nearly 100% of available data and reduce the rms scatter several times smaller than archival light curves for brighter stars. This new data set gives a rare opportunity to explore different types of variability of short (~minutes) and long (~1 month) time scales in open cluster stars.
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