Image reduction pipeline for the detection of variable sources in highly crowded fields

We present a reduction pipeline for CCD (charge-coupled device ) images which was built to search for variable sources in highly crowded fields like the M 31 bulge and to handle extensive databases due to large time series. We describe all steps of the standard reduction in detail with emphasis on the realisation of per pixel error propagation: Bias correction, treatment of bad pixels, flatfielding, and filtering of cosmic rays. The problems of conservation of PSF (point spread function) and error propagation in our image alignment procedure as well as the detection algorithm for variable sources are discussed: we build difference images via image convolution with a technique called OIS (optimal image subtraction, Alard & Lupton [CITE]), proceed with an automatic detection of variable sources in noise dominated images and finally apply a PSF-fitting, relative photo metry to the sources found. For the WeCAPP project (Riffeser et al. [CITE]) we achieve detections for variable sources with an apparent brightness of e.g. at their minimum and a variation of (or brightness minimum and a variation of ) on a background signal of based on a exposure with seeing at a telescope. The complete per pixel error propagation allows us to give accurate errors for each measurement.