D. D. Dixon, W. N. Johnson, J. D. Kurfess, R. K. Piña, R. C. Puetter,
W. R. Purcell, T. O. Tümer, Wm. A. Wheaton, A. D. Zych
A&A Supplement, 128, C683 (1996)
Abstract
We describe a new technique, pixon-based deconvolution, for the reconstruction
of images and spectra from low signal-to-noise data. In ``traditional''
techniques, such as Maximum Likelihood (ML) and Maximum Entropy (ME),
the model parameters (e.g., pixel size) are considered ``nuisance
parameters'', and generally held fixed in the course of deconvolution.
Pixon techniques allow the model parameters to change according to
the information contained in the data. The pixon model admits only
that level of detail which is statistically justified by the data,
thus greatly reducing the production of spurious sources and signal
correlated residuals, common problems in ML and ME reconstructions.
As a result, gains in sensitivity and spatial/energy resolution may
be realized. Sample applications to data from OSSE and COMPTEL will be
presented.