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The Chinese Small Telescope ARray (CSTAR) carried out high-cadence time-series observations of 27 square degrees centered on the South Celestial Pole during the Antarctic winter seasons of 2008, 2009 and 2010. Aperture photometry of the 2008 and 2010 i-band images resulted in the discovery of over 200 variable stars. Yearly servicing left the array defocused for the 2009 winter season, during which the system also suffered from intermittent frosting and power failures. Despite these technical issues, nearly 800,000 useful images were obtained using g, r & clear filters. We developed a combination of difference imaging and aperture photometry to compensate for the highly crowded, blended and defocused frames. We present details of this approach, which may be useful for the analysis of time-series data from other small-aperture telescopes regardless of their image quality. Using this approach, we were able to recover 68 previously-known variables and detected variability in 37 additional objects. We also have determined the observing statistics for Dome A during the 2009 winter season; we find the extinction due to clouds to be less than 0.1 and 0.4 mag for 40% and 63% of the dark time, respectively.
We analyze the data of the gravitational microlensing survey carried out by by the MOA group during 2000 towards the Galactic Bulge (GB). Our observations are designed to detect efficiently high magnification events with faint source stars and short
We present a selection of methods for automatically constructing an optimal kernel model for difference image analysis which require very few external parameters to control the kernel design. Each method consists of two components; namely, a kernel d
The Chinese Small Telescope ARray (CSTAR) carried out high-cadence time-series observations of 20.1 square degrees centered on the South Celestial Pole during the 2008, 2009 & 2010 winter seasons from Dome A in Antarctica. The nearly-continuous 6 mon
Over the last two decades the Andromeda Galaxy (M31) has been something of a test-bed for methods aimed at obtaining accurate time-domain relative photometry within highly crowded fields. Difference imaging methods, originally pioneered towards M31,
We present a comparison of several Difference Image Analysis (DIA) techniques, in combination with Machine Learning (ML) algorithms, applied to the identification of optical transients associated with gravitational wave events. Each technique is asse