Deconvolution of JWST/MIRI Images: Applications to an AGN Model and GATOS Observations of NGC 5728 (2312.14307v2)

Abstract: The superb image quality, stability and sensitivity of the JWST permit deconvolution techniques to be pursued with a fidelity unavailable to ground-based observations. We present an assessment of several deconvolution approaches to improve image quality and mitigate effects of the complex JWST point spread function (PSF). The optimal deconvolution method is determined by using WebbPSF to simulate JWST's complex PSF and MIRISim to simulate multi-band JWST/Mid-Infrared Imager Module (MIRIM) observations of a toy model of an active galactic nucleus (AGN). Five different deconvolution algorithms are tested: (1) Kraken deconvolution, (2) Richardson-Lucy, (3) Adaptive Imaging Deconvolution Algorithm, (4) Sparse regularization with the Condat-V~u algorithm, and (5) Iterative Wiener Filtering and Thresholding. We find that Kraken affords the greatest FWHM reduction of the nuclear source of our MIRISim observations for the toy AGN model while retaining good photometric integrity across all simulated wavebands. Applying Kraken to Galactic Activity, Torus, and Outflow Survey (GATOS) multi-band JWST/MIRIM observations of the Seyfert 2 galaxy NGC 5728, we find that the algorithm reduces the FWHM of the nuclear source by a factor of 1.6-2.2 across all five filters. Kraken images facilitate detection of a SE to NW $\thicksim$2".5 ($\thicksim$470 pc, PA $\simeq$115\deg) extended nuclear emission, especially in the longest wavelengths. We demonstrate that Kraken is a powerful tool to enhance faint features otherwise hidden in the complex JWST PSF.