Analysing GREAT08 with KSBf90:

1) Source Extraction and Star selection

For GREAT08 we provide an SExtractor source catalogue tarball to avoid this stage.  Unpack GREAT08_SExtractor_files.tar in your $CATDIR.  This tarball contains source catalogues for all the GREAT08 images and three files which list the stellar criteria that we use to select stars from the star fits images.

2) Measure the PSF shape: psffit.f90

In /home/user/GREAT08/GREAT08_scripts you can run a script called runstars.scr.  This will measure the shapes of the stars in the three different GREAT08 star images and different scale radii.  The output are files containing polynomial fits to the PSF pattern as a function of scale radii.  For GREAT08 we fit a zeroth order polynomial as the PSF pattern is constant (this will change in GREAT09).  Postscript files are also output to show the PSF pattern before and after correction and the variation with scale radii.  Both these outputs are stored in your $CATDIR/Stars.

3) Measure the galaxy shape and correct for the PSF distortion:  gal_correct.f90

In /home/user/GREAT08/GREAT08_scripts you can run a script called rungals.scr.  This will measure the shapes of the galaxies in the RealNoise_Known, LowNoise_Known or LowNoise_Blind, depending on your choice of $dataset at the top of the script.  For RealNoise_Blind, as the PSF changes you need to run a different script called rungals_RealNoise_Blind.scr.   This code will use the PSF model from psffit.f90 ($CATDIR/Stars/psf*.psffit.cat) to correct for the PSF distortion.  The output from this code are files appending shape measurement information to the original SExtractor catalogues, stored in you $CATDIR/*Noise_*/.  The code also writes an output to the screen of the average shear in each image.  You can pipe this to a file to submit to the leaderboard.

4) Example: v1.0/LowNoise_Blind/set0001.fit

For this image KSBf90 finds g1 = 0.0316081 +/- 0.0023853

                                              g2 = -0.0327951 +/- 0.0026978

Page maintained by Catherine Heymans, IfA, University of Edinburgh