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| Paper: |
Multiscale Source Detection for Long Wavelength Astronomical Images |
| Volume: |
485, Astronomical Data Analysis Software and Systems XXIII |
| Page: |
421 |
| Authors: |
Masias, M.; Freixenet, J.; Peracaula, M.; Lladó, X. |
| Abstract: |
The increasing number of astronomical in mid- and far-infrared,
as well as in submillimeter and radio wavelengths, brings more
difficulties to the already challenging task of detecting sources in an
automatic way. These specific images are characterized by presenting a
more complex background than in shorter wavelengths, with a higher
component of noise, more noticeable flux variations and both unresolved
and extended sources with a higher dynamic range. Aiming to improve the
source detection efficiency in long wavelength images, in this paper we
present a new approach based on the combined use of multiscale
decomposition and a recently developed method called Distilled
Sensing. Its application minimizes the impact of the contaminants from
the background, unveiling and highlighting the sources at the same
time. The experimental results achieved using infrared and radio
aperture synthesis images illustrate the good performance of the
approach, correctly identifying a greater percentage of true sources
than using both the widely used SExtractor algorithm and the Distilled
Sensing method alone. |
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