Conversion of Stackfit to LSST software stack

Status as of Feb 20, 2012

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).

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.

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.

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.

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 ...

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.

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.