Mathematics – Probability
Scientific paper
May 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21641502c&link_type=abstract
American Astronomical Society, AAS Meeting #216, #415.02; Bulletin of the American Astronomical Society, Vol. 41, p.825
Mathematics
Probability
Scientific paper
We present a new astronomical object detection and deblending algorithm. The algorithm fits PSF-convolved Sérsic profiles to source candidates. An iterative approach is used to subtract the light profile from each source in order to deblend projected objects. To minimize spurious detections, the optimal number of objects is determined by maximizing the posterior probability based on Bayesian analysis. This new method also minimizes the amount and complexity of real-time user input with respect to many commonly used source detection algorithms. This apppoach can have great impact when processing large data-sets and data-streams from next generation telescopes, such as the LSST and the E-ELT.
Cabrera Guillermo
Harrison Craig
Miller Christopher J.
Vera Emilio
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