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We present a reduction pipeline for CCD (charge-coupled device) images whichwas built to search for variable sources in highly crowded fields like the M31bulge and to handle extensive databases due to large time series. We describeall steps of the standard reduction in detail with emphasis on the realisationof 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 procedureas well as the detection algorithm for variable sources are discussed: We builddifference images via image convolution with a technique called OIS (Alard &Lupton, 1998), proceed with an automatic detection of variable sources in noisedominated images and finally apply a PSF-fitting, relative photometry to thesources found. For the WeCAPP project (Riffeser et al., 2001) we achieve 3sigma detections for variable sources with an apparent brightness of e.g. m =24.9 mag at their minimum and a variation of dm = 2.4 mag (or m = 21.9 magbrightness minimum and a variation of dm = 0.6 mag) on a background signal of18.1 mag/arcsec^2 based on a 500 s exposure with 1.5 arcsec seeing at a 1.2 mtelescope. The complete per pixel error propagation allows us to give accurateerrors for each measurement.Comment: 15 pages, 4 figures, accepted by A&

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