Image reduction pipeline for the detection of variable sources in highly crowded fields
Universitäts-Sternwarte München, Scheinerstr. 1, 81679 München, Germany
Corresponding author: C. A. Gössl, cag@usm.uni-muenchen.de
Received:
6
August
2001
Accepted:
23
October
2001
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.
Key words: methods: data analysis / methods: observational / techniques: image processing / techniques: error propagation / techniques: optimal image subtraction
© ESO, 2002