Bayesian Inference of Galaxy Morphology

Details Bayesian Inference of Galaxy Morphology Bayesian Inference of Galaxy Morphology

Jan 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aas...21743034y&link_type=abstract

American Astronomical Society, AAS Meeting #217, #430.34; Bulletin of the American Astronomical Society, Vol. 43, 2011

Statistics

Computation

Scientific paper

Reliable inference on galaxy morphology from quantitative analysis of ensemble galaxy images is challenging but essential ingredient in studying galaxy formation and evolution, utilizing current and forthcoming large scale surveys. To put galaxy image decomposition problem in broader context of statistical inference problem and derive a rigorous statistical confidence levels of the inference, I developed a novel galaxy image decomposition tool, GALPHAT (GALaxy PHotometric ATtributes) that exploits recent developments in Bayesian computation to provide full posterior probability distributions and reliable confidence intervals for all parameters. I will highlight the significant improvements in galaxy image decomposition using GALPHAT, over the conventional model fitting algorithms and introduce the GALPHAT potential to infer the statistical distribution of galaxy morphological structures, using ensemble posteriors of galaxy morphological parameters from the entire galaxy population that one studies.

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