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We introduce new methods for robust high-precision photometry fromwell-sampled images of a non-crowded field with a strongly varying point-spreadfunction. For this work, we used archival imaging data of the open cluster M37taken by MMT 6.5m telescope. We find that the archival light curves from theoriginal image subtraction procedure exhibit many unusual outliers, and morethan 20% of data get rejected by the simple filtering algorithm adopted byearly analysis. In order to achieve better photometric precisions and also toutilize all available data, the entire imaging database was re-analyzed withour time-series photometry technique (Multi-aperture Indexing Photometry) and aset of sophisticated calibration procedures. The merit of this approach is asfollows: we find an optimal aperture for each star with a maximumsignal-to-noise ratio, and also treat peculiar situations where photometryreturns misleading information with more optimal photometric index. We alsoadopt photometric de-trending based on a hierarchical clustering method, whichis a very useful tool in removing systematics from light curves. Our methodremoves systematic variations that are shared by light curves of nearby stars,while true variabilities are preserved. Consequently, our method utilizesnearly 100% of available data and reduce the rms scatter several times smallerthan archival light curves for brighter stars. This new data set gives a rareopportunity to explore different types of variability of short (~minutes) andlong (~1 month) time scales in open cluster stars.

the astronomical journal

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