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Gössl, C. A. & Riffeser, A. 2003, in ASP Conf. Ser., Vol. 295 Astronomical Data Analysis Software and Systems XII, eds. H. E. Payne, R. I. Jedrzejewski, & R. N.
Hook (San Francisco: ASP), 229
Image Reduction Pipeline for the Detection of Variable Sources
in Highly Crowded Fields
Claus A. Gössl, Arno Riffeser
Universitäts-Sternwarte München, Scheinerstraße 1,
D-81671 München, Germany
Abstract:
We present a reduction pipeline for CCD (charge-coupled device) images
which was built to search for variable sources in highly crowded fields
such as the M 31 bulge. We describe all the steps of the standard reduction
including per pixel error propagation: Bias correction, treatment of bad
pixels, flatfielding, and filtering of cosmic ray events. We utilize
a flux and PSF (point spread function) conserving alignment procedure
and a signal-to-noise maximizing stacking method. We build difference
images via image convolution with a technique called OIS (optimal image
subtraction, Alard & Lupton 1998), proceed with PSF-fitting, relative
photometry on all pixels and finally apply an automatic detection of
variable sources. The complete per pixel error propagation allows us
to give accurate errors for each measurement.
The WeCAPP project (Riffeser et al. 2001),
which imaged the M 31 bulge to search for
Microlensing events, yielded 0.2 TB of inhomogenous raw data.
Available data reduction software was not able to cope with the
highly variable observing conditions
(varying seeing, sky background level, and flatfield quality; different
cameras, CCDs, and telescopes) and give consistent measurements with
reliable error estimates.
Therefore we decided to develop our own reduction pipeline.
For additional information on error propagation and why it is
important see also Moshir et al. (2003) and Gössl & Riffeser (2002).
2.1 Bad Pixels & Bias Correction
We mask saturated (and blooming affected) pixels, as well as
CCD-defects (hot, cold pixels etc.).
We subtract the bias level of individual frames estimated from the
overscan region and a masterbias
(
-clipped mean image of multiple bias level corrected
bias frames).
The initial error estimate for each pixel in every image is calculated
from the pixel's photon noise (
),
the bias noise of the image (clipped RMS of the overscan), and the
uncertainties of bias level and bias pattern determination.
Errors are propagated throughout the complete reduction pipeline with
Gaussian error propagation.
To achieve a high signal-to-noise ratio (
) for a combined
flatfield of an epoch we first calculate in
each pixel the error weighted mean of normalized and illumination
corrected twilight flatfields.
After rejecting all
pixels regions where the center pixel
exceeds this mean by more than
, the final calibration image
is built by
clipping of the remaining pixels.
We fit five-parameter Gaussians to all local maxima of an image.
Sources with a width along one axis of the fitting function smaller
than a threshold
(which has to be chosen according to the PSF)
and, in addition,
an amplitude of the fitting function exceeding the expected noise by a
certain factor
(which has to be chosen according to the additional noise, i.e., due to
crowding) correspond to cosmics.
We mask the pixels,
where the fitting function exceeds the fitted surface constant by more
than two times the expected photon noise.
2.5 Image Alignment & Stacking
Images are shifted onto a reference grid using a flux and PSF
conserving algorithm.
The shifted images are photometrically calibrated using the profile of
the M 31 bulge.
Bad pixels (except saturated) are replaced with pixels of the most
similar image, but accounted for in the error image.
The final stack is built by maximizing its
ratio using the error
images and the PSF width for the calculation of weighting factors
(Figure 1).
Figure:
Left:
pixel window of a raw CCD image of part
of the M 31 bulge taken at the Calar Alto 1.23 m telescope,
3. Feb. 2001. (WeCAPP project, Riffeser et al. 2001).
Right:
Stacked image after processing steps described in Sect. 2.1. to
Sect. 2.5..
![\begin{figure}
\epsscale{0.8}
\plottwo{P4.4_f1a.eps}{P4.4_f1b.eps}
\end{figure}](img16.gif) |
For the difference photometry a high
reference frame with a
narrow PSF is convolved to the broader PSF of each science frame.
The calculation of the convolution kernel is performed by a least
squares linear fitting procedure optimizing 52 free parameters (OIS).
The difference frame (built by subtracting the convolved reference
frame from the science frame) shows a large number of positive and
negative point sources.
Figure 2:
Left: Profile fitting photometry
(cuts:
Jy,
Jy).
Right: Corresponding error frame
(cuts:
Jy,
Jy).
![\begin{figure}
\epsscale{0.8}
\plottwo{P4.4_f2a.eps}{P4.4_f2b.eps}
\end{figure}](img17.gif) |
Fluxes for the variable sources are extracted using
PSF-fitting photometry in each pixel:
The PSF of a high
star in the convolved reference frame is fit
to a small region around each pixel in the difference image
(Figure 2).
This reduces the influence of neighboring variable sources to a low
level.
Therefore we are able to extract light curves for each pixel of the
difference frame (Figure 3).
Figure:
Final light curve of a long period, semi-regular variable
star (in the center of Figure 2).
The `
' symbol shows the epoch of the sample images.
The sample source in the sample image shows a difference flux of
Jy
on a background of
Jy/arcsec
.
![\begin{figure}
\epsscale{0.62}
\plotone{P4.4_f3.eps}
\end{figure}](img18.gif) |
All algorithms are implemented in C++.
Each individual reduction step is represented by a command line
program.
The pipeline is a simple shell script or Makefile.
We take part in the development of a Little Template
Library (LTL) which provides very fast and easy to use methods
for I/O (i.e., FITS or ASCII), array operations, statistics and Linear
Algebra as well as for command line flags and configuration file parameters.
Acknowledgments
Our thanks are due to Ralf Bender, Niv Drory, Jürgen Fliri,
Ulrich Hopp, and Jan Snigula.
This work was supported by the German Deutsche
Forschungsgemeinschaft, DFG, SFB 375
Astroteilchenphysik.
References
Alard, C., & Lupton, R. H. 1998, ApJ, 503, 325
Gössl, C. A., & Riffeser A. 2002, A&A, 381, 1095
Moshir, M., Fowler, J., & Henderson, D. 2003, this volume, 181
Riffeser, A., Fliri, J., Gössl, C. A., Bender, R.,
Hopp, U., Bärnbantner, O., Ries, C., Barwig, H., Seitz, S., &
Mitsch, W. 2001, A&A, 379, 362
© Copyright 2003 Astronomical Society of the Pacific, 390 Ashton Avenue, San Francisco, California 94112, USA
Next: Status of the BIMA Image Pipeline
Up: Data Management and Pipelines
Previous: The Raptor Real-Time Processing Architecture
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