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Paper: New Probabilistic Galaxy Classification in Large Photometric Surveys
Volume: 475, Astronomical Data Analysis Software and Systems XXII
Page: 21
Authors: Liang, F.; Brunner, R. J.
Abstract: A number of different projects have or soon will map the sky in part to better constrain the cosmological parameters driving the evolution of our Universe. One of the most important and least quantified steps in this process is the task of efficiently identifying galaxies within these large data sets. Generally, simple parameter cuts have been used, for example the SDSS cut on the difference between a PSF and model magnitude in the r-band. While this approach can be efficiently implemented and is easy to understand, it has been shown to be ineffective at brighter magnitudes than originally suspected. Thus, we are applying powerful statistical techniques such as support vector machine and non-parametric bayesian clustering to this challenge with the goal of developing a probabilistic galaxy classification that can be reliably extended to fainter magnitudes, thereby increasing the precision of future cosmological measurements.
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