Improved time-series photometry and calibration method for non-crowded fields

Abstract

We present a new photometric reduction method for precise time-series photometry of non-crowded fields that does not need to involve relatively complicated and CPU intensive techniques such as point-spread-function (PSF) fitting or difference image analysis. This method, which combines multi-aperture index photometry and a spatio-temporal de-trending algorithm, gives much superior performance in data recovery and light-curve precision. In practice, the brutal filtering that is often applied to remove outlying data points can result in the loss of vital data, with seriously negative impacts on short-term variations such as flares. Our method utilizes nearly 100% of available data and reduces the rms scatter to several times smaller than that for archived light curves for brighter stars. We outline the details of our new method, and apply it to cases of sample data from the MMT survey of the M37 field, and the HAT-South survey. Show more