Using PSF Grids

WebbPSF includes functionality designed to work with the Photutils package to enable precise PSF-fitting photometry and astrometry. This makes use of the GriddedPSFModel class (available in Photutils > 0.6), which implements a version of the empirical or effective PSF (“ePSF”) modeling framework pioneered by Jay Anderson, Ivan King, and collaborators. This approach has been highly successful with HST and other space observatories, and we expect it will also be productive with JWST. In practice we will want to use ePSF models derived from real observations, but for now we can make them in simulation.

The first step is to create a grid of fiducial PSFs spanning the instrument/detector of choice. This can be done using the psf_grid() method which will output a (list of or single) photutils GriddedPSFModel object(s). Users can then use photutils to apply interpolation to the grid to simulate a spatially dependent PSF anywhere on the instrument, without having to perform a full PSF calculation at each location. This faster approach is critical if you’re dealing with potentially tens of thousands of stars scattered across many megapixels of detector real estate.

Jupyter Notebook

See this Gridded PSF Library tutorial notebook for more details and example code.

Example PSF grid

PSF grid calculations are useful for visualizing changes in the PSF across instrument fields of view. Here’s one example of that.

nrc = webbpsf.NIRCam()
nrc.filter='F212N'
nrc.detector='NRCA3'
grid = nrc.psf_grid(num_psfs=36, all_detectors=False)
webbpsf.gridded_library.display_psf_grid(grid)
Example grid of NIRCam PSFs: 6x6 grid across NRCA3

Figure 1: An example of grid calculated across the NRCA3 detector in NIRCam. These PSFs are all very similar.

Example grid of NIRCam PSF differencess: 6x6 grid across NRCA3

Figure 2: By subtracting off the average PSF, the subtle differences from point to point become clear. The PSF is sharpest in the upper left corner of this detector.