  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.
