A New Catalog of Variable Stars in the Field of the Open Cluster M37

We present a comprehensive re-analysis of stellar photometric variability in the field of the open cluster M37 following the application of a new photometry and de-trending method to MMT/Megacam image archive. This new analysis allows a rare opportunity to explore photometric variability over a broad range of time-scales, from minutes to a month. The intent of this work is to examine the entire sample of over 30,000 objects for periodic, aperiodic, and sporadic behaviors in their light curves. We show a modified version of the fast $chi^{2}$ periodogram algorithm (F$chi^{2}$) and change-point analysis (CPA) as tools for detecting and assessing the significance of periodic and non-periodic variations. The benefits of our new photometry and analysis methods are evident. A total of 2306 stars exhibit convincing variations that are induced by flares, pulsations, eclipses, starspots, and unknown causes in some cases. This represents a 60% increase in the number of variables known in this field. Moreover, 30 of the previously identified variables are found to be false positives resulting from time-dependent systematic effects. New catalog includes 61 eclipsing binary systems, 92 multiperiodic variable stars, 132 aperiodic variables, and 436 flare stars, as well as several hundreds of rotating variables. Based on extended and improved catalog of variables, we investigate the basic properties (e.g., period, amplitude, type) of all variables. The catalog can be accessed through the web interface (http://stardb.yonsei.ac.kr/).

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

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.