A Deconvolution Technique to Correct Deep Images of Galaxies from Instrumental Scattered Light

Abstract

Deep imaging of the diffuse light emitted by the stellar fine structures and outer halos around galaxies is now often used to probe their past mass assembly. Because the extended halos survive longer than the relatively fragile tidal features, they trace more ancient mergers. We use images reaching surface brightness limits as low as 28.5-29 mag.arcsec-2 (g-band) to obtain light and color profiles up to 5-10 effective radii of a sample of nearby early-type galaxies. They were acquired with MegaCam as part of the CFHT MATLAS large programme. These profiles may be compared to those produced by simulations of galaxy formation and evolution, once corrected for instrumental effects. Indeed they can be heavily contaminated by the scattered light caused by internal reflections within the instrument. In particular, the nucleus of galaxies generates artificial flux in the outer halo, which has to be precisely subtracted. We present a deconvolution technique to remove the artificial halos that makes use of very large kernels. The technique based on PyOperators is more time efficient than the model-convolution methods also used for that purpose. This is especially the case for galaxies with complex structures that are hard to model. Having a good knowledge of the Point Spread Function (PSF), including its outer wings, is critical for the method. A database of MegaCam PSF models corresponding to different seeing conditions and bands was generated directly from the deep images. It is shown that the difference in the PSFs in different bands causes artificial changes in the color profiles, in particular a reddening of the outskirts of galaxies having a bright nucleus. The method is validated with a set of simulated images and applied to three representative test cases: NGC 3599, NGC 3489, and NGC 4274, and exhibiting for two of them a prominent ghost halo. The method successfully removes it.

arXiv comment10 pages, 11 figures, accepted for publication in A&A

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