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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