Using PSF Grids

STPSF includes functionality designed to work with the Photutils package to enable precise PSF-fitting photometry and astrometry. This makes use of the GriddedPSFModel class, 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 <https://stpsf.readthedocs.io/en/latest/api/stpsf.SpaceTelescopeInstrument.html#stpsf.SpaceTelescopeInstrument.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

The psf_grid method is used to compute a PSF grid. It takes many of the same arguments as does calc_psf. In addition, it takes a num_psfs argument for the number of PSF to include in the grid. This must be a square integer; for instance num_psfs=9 will compute a 3x3 grid, and so on. Here’s one example of that.

%matplotlib inline
import stpsf

nrc = stpsf.NIRCam()
nrc.filter='F212N'
nrc.detector='NRCA3'

grid = nrc.psf_grid(num_psfs=36, all_detectors=False, verbose=False)

The returned object is a Photutils ePSF model and can be used with the functions in photutils for PSF-fitting photometry and astrometry.

Photutils also includes convenient functions for displaying PSF grids. PSF grid calculations are useful for visualizing changes in the PSF across instrument fields of view.

grid.plot_grid();

Above: An example of grid calculated across the NRCA3 detector in NIRCam. These PSFs are all very similar to the eye.

We can adjust the display to highlight the differences, and thus make the field dependence easier to see:

grid.plot_grid(deltas=True);

By subtracting off the average PSF, the subtle model-predicted differences from point to point become clear.