Conversion of Stackfit to LSST software stack

1. Conversion of Stackfit to LSST software stack Status as of Feb 20, 2012

2. Goal of this projects • Implement James Jee’s “Stackfit” algorithm in the LSST software stack. • Demonstrate consistency between the LSST version and James’ implementation by direct comparison of PSF models, stacks, and shape measurements. • Initial implementation to be done on DLS data (from F2 field).

3. Key Components • Create calibrated exposure for each CCD image (247 CCD images in DLS F2p23). • Create spatially-varying PSF model for each CCD image. • Create weighted coadd of the entire field. • Create a source catalog from the coadd. • Create similarly weighted coadd of psfs for each object in the source catalog.

4. Key Components(cont’d) • Estimate the e1 and e2 components for each source in the coadd catalog. • The estimate is done by fitting a 7 parameter Gaussian or 9 parameter Sersic model. • The data is a cutout of each object with a square 4 * A, where A is the major axis dimension, estimated from moments. • The model is created by convolving a model matrix of the same size as the cutout with the stacked PSF for that object.

5. Status of PSF estimation • Have run the LSST psf modeling code on 10,000 objects from DLS F2p23. • The psfs models were used to create kernels at the centroids of bright objects. • The kernels were compared with the kernels from James’ psf catalogs by fitting to 2D elliptical Gaussian model • The difference in the estimates of σA and σB were almost uniformly smaller than the parameter errors.

6. Status of Calibration/Stacking • Used LSST software to make calibrated exposures from 30 DLS images. • The CCD astrometric calibration is the most important part. I used the DLS star catalog for calibration, I created a TAN-SIP correction. • The results were rather spotty – I concluded that we need to be able to combine multiple exposures from each CCD to model distortions more accurately. • The resulting stacks are not good enough for our purpose. However, I moved on ...

7. Status of shape measurement • Able to build stacks and create source catalogs. • Able to create psf stacks for each source. • Applied Minuit2 minimizer to the estimate a 2D Gaussian profile for each source. MiGrad does not converge very reliably. • Imported C translation of mpfit. This code is faster and converges more reliably.

8. Performance Assessment • Ran 1000 sources with footprints > 20 pixels for a 7 parameter Gaussian model. • The Python wrapped Mpfit code did the parameter fits in 191 s. • Ran 100 sources with footprints > 500 pixels. Mpfit took 65 seconds. • This was on a single core of an AMD Opteron 2427 2.2 GHz system with 32 GB of memory.