3-D deconvolution of hyper-spectral astronomical data

TL;DR

This paper introduces a flexible, regularized forward fitting method for hyper-spectral image restoration, effectively reducing bias and residuals, with broad applicability to astronomical data and imaging techniques.

Contribution

The paper presents a novel regularized chi2-based forward fitting approach with separable regularizations, adaptable to various hyper-spectral data and instrumental contexts.

Findings

Achieves sub-percent residuals in synthetic filters

Meets strict bias and photometricity requirements

Applicable to multiple hyper-spectral imaging scenarios

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.