Automated Detection of Objects Based on Sérsic Profiles

Details Automated Detection of Objects Based on Sérsic Profiles Automated Detection of Objects Based on Sérsic Profiles

Jan 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aas...21734413c&link_type=abstract

American Astronomical Society, AAS Meeting #217, #344.13; Bulletin of the American Astronomical Society, Vol. 43, 2011

Computer Science

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Scientific paper

We present the results of a new astronomical object detection and deblending algorithm when applied to Sloan Digital Sky Survey data. Our algorithm fits PSF-convolved Sérsic profiles to elliptical isophotes of source candidates. The main advantage of our method is that it minimizes the amount and complexity of real-time user input relative to many commonly used source detection algorithms. Our results are compared with 1D radial profile Sérsic fits. Our long-term goal is to use these techniques in a mixture-model environment to leverage the speed and advantages of machine learning. This approach will have a great impact when re-processing large data-sets and data-streams from next generation telescopes, such as the LSST and the E-ELT.

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