PSFr - Point Spread Function reconstruction¶

https://github.com/sibirrer/psfr/blob/main/docs/_static/stacked_psf_animation.gif

Point Spread Function reconstruction for astronomical ground- and space-based imaging data.

Example¶

# get cutout stars in the field of a JWST observation (example import)
from psfr.util import jwst_example_stars
star_list_jwst = jwst_example_stars()

# run PSF reconstruction (see documentation for further options)
from psfr.psfr import stack_psf
psf_moswl, center_list, mask_list = stack_psf(star_list_jwst, oversampling=4,
                                              saturation_limit=None, num_iteration=50)

We further refer to the example Notebook and the Documentation.

Features¶

Iterative PSF reconstruction given cutouts of individual stars or other point-like sources.
Sub-pixel astrometric shifts calculated and accounted for while performing the PSF reconstruction.
PSF reconstruction available in super-sampling resolution.
Masking pixels, saturation levels and other options to deal with artifacts in the data.

Used by¶

PSFr is in use with James Webb Space Telescope imaging data (i.e., Santini et al. 2022, Merlin et al. 2022, Yang et al. 2022). The iterative PSF reconstruction procedure was originally developed and used for analyzing strongly lensed quasars (i.e., Birrer et al. 2019 , Shajib et al. 2018 , Shajib et al. 2019 , Schmidt et al. 2022).

Other resources¶

We also refer to the astropy core package photutils and in particular to the empirical PSF module ePSF .

Credits¶

The software is an off-spring of the iterative PSF reconstruction scheme of lenstronomy, in particular its psf_fitting.py functionalities.

If you make use of this software, please cite: ‘This code is using PSFr (Birrer et al. in prep) utilizing features of lenstronomy (Birrer et al. 2021)’.