We constructed convolution kernels for the most common instrumental PSFs. They facilitate transforming images from different telescopes into a common PSF. Each pixel in the convolved images will observe the same sky region across the different instruments.
We constructed convolution-kernels for the following instruments:
Details on the kernel construction, performance analysis, and recommended usage, are available in our paper
G. Aniano, B.T. Draine, K.D. Gordon, K. Sandstrom (2011): Publ. Astr. Soc. Pac., Vol 123, pp.1218-1236.
Please cite our paper if you benefit from the ideas presented in our work, or the constructed kernels.
The current kernels were constructed using the same algorithms as described in our paper. However, we have updated them to the latest PSF characterization of each instrument (as of October 2020). Their performance should be better than those described in the paper. They have been constructed carefully and subject to a large number of checks, but no guarantee as to their accuracy is given. Use them at your own risk.
Comments and suggestions are highly appreciated. Gonzalo Aniano is taking a hiatus from academia, however, he will try to respond to questions and comments. He can be reached at ganiano [at] gmail.com, or via LinkedIn.
We wrote a small IDL package (convolve_image.pro). It loads an image, loads a kernel, perform all the necessary transformations, and returns a convolved image. Several people have tested it with several images and it has always worked well. However, as any other routine, it may have bugs. Always inspect the resulting images to make sure it worked properly. Make sure your image are in surface brightness units.