Convolution kernels for multi-wavelength imaging

¹ Institut d’Astrophysique Spatiale, CNRS, UMR 8617, Univ. Paris-Sud, Université Paris-Saclay, IAS, Bât. 121, Univ. Paris-Sud, 91405 Orsay, France
e-mail: alexandre.boucaud@ias.u-psud.fr
² Sorbonne Universités, UPMC Univ Paris 6 et CNRS, UMR 7095, Institut d’Astrophysique de Paris, 98bis bd Arago, 75014 Paris, France
³ Laboratoire des Signaux et Systèmes (Univ. Paris-Sud, CNRS, CentraleSupélec, Université Paris-Saclay), 91192 Gif-sur-Yvette, France

Received: 9 June 2016
Accepted: 5 September 2016

Abstract

Astrophysical images issued from different instruments and/or spectral bands often require to be processed together, either for fitting or comparison purposes. However each image is affected by an instrumental response, also known as point-spread function (PSF), that depends on the characteristics of the instrument as well as the wavelength and the observing strategy. Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been acquired by the same instrument. We propose an algorithm that generates such PSF-matching kernels, based on Wiener filtering with a tunable regularisation parameter. This method ensures all anisotropic features in the PSFs to be taken into account. We compare our method to existing procedures using measured Herschel/PACS and SPIRE PSFs and simulated JWST/MIRI PSFs. Significant gains up to two orders of magnitude are obtained with respect to the use of kernels computed assuming Gaussian or circularised PSFs. A software to compute these kernels is available at https://github.com/aboucaud/pypher