To be published in:
A method for spatial deconvolution of spectra.
M. Kirkove2,** and
1 Universidad Católica de Chile, Departamento de Astronomia y Astrofisica, Casilla 306, Santiago 22, Chile.
2 Institut d'Astrophysique et de Géophysique - Université de Liège, Avenue de Cointe 5, 4000 Liège, Belgium
* Maître de Recherches FNRS, Belgium
** Aspirant FNRS, Belgium
A method for spatial deconvolution of spectra is presented. It
follows the same fundamental principles as the "MCS image
deconvolution algorithm" (Magain, Courbin, Sohy, 1998) and uses
information contained in the spectrum of a reference Point Spread
Function (PSF) to spatially deconvolve spectra of very blended
sources. An improved resolution rather than an infinite one is aimed
at, overcoming the well known problem of "deconvolution artefacts".
As in the MCS algorithm, the data are decomposed into a sum of
analytical point sources and a numerically deconvolved background, so
that the spectrum of extended sources in the immediate vicinity of
bright point sources may be accurately extracted and sharpened. The
algorithm has been tested on simulated data including seeing variation
as a function of wavelength and atmospheric refraction. It is shown
that the spectra of severely blended point sources can be resolved
while fully preserving the spectrophotometric properties of the data.
Extended objects "hidden" by bright point sources (up to 4-5
magnitudes brighter) can be accurately recovered as well, provided the
data have a sufficiently high total signal-to-noise ratio (200-300 per
spectral resolution element). Such spectra are relatively easy to
obtain, even down to faint magnitudes, within a few hours of
integration time with 10m class telescopes.
Data processing: spectroscopy - deconvolution
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