3D deconvolution of hyper-spectral astronomical data

Abstract

In this paper we present a general forward fitting method for multichannel image restoration based on regularized chi2. We introduce separable regularizations that account for the dynamic of the model and take advantage of the continuities present in the data, leaving only two hyper-parameters to tune. We illustrate a practical implementation of this method in the context of host galaxy subtraction for the Nearby SuperNova factory. We show that the image restoration obtained fulfills the stringent requirements on bias and photometricity needed by this program. The reconstruction yields sub-percent integrated residuals in all the synthetic filters considered both on real and simulated data. Even though our implementation is tied to the SNfactory data, the method translates to any hyper-spectral data. As such, it is of direct relevance to several new generation instruments like MUSE. Also, this technique could be applied to multi-band astronomical imaging for which image reconstruction is important, for example to increase image resolution for weak lensing surveys.