IACAT: Collaboratively Exploring the Dark Universe
College: Liberal Arts and Sciences
Award year: 2012-2013
A number of major efforts are under way to characterize the nature of dark matter and dark energy that leverage correlation measurements of large photometric datasets. The Sloan Digital Sky Survey (SDSS) has imaged over one-quarter of the entire sky, producing a catalog of several hundred million sources. Starting later this year, the Dark Energy Survey (DES) will generate a photometric data set that will be nearly an order of magnitude larger than the SDSS. Later this decade, the Large Synoptic Survey Telescope (LSST) project will generate photometric data for billions of galaxies. Illinois is a major partner in both the DES and LSST surveys, and NCSA will be the data home for each. Often, the first response to this wealth of data is to use simple approaches to quickly begin to understand the data: for example, using a simple parameter cut to distinguish between stars and galaxies. This project seeks to develop a more robust star/galaxy classification method. The first step is to determine the optimal subset of measured attributes to use in our Bayesian classification framework. The second component of our proposed classification research is the identification of the optimal prior information. The third component of our proposed classification research is the proper modeling of subcomponents of our likelihood function. The fourth major aspect of our proposed research is the identification of a suitable technique for the inclusion of magnitude errors in the likelihood calculations. By doing this, we will be better positioned to characterize the effect of the observational uncertainty on source classification. The final aspect of this proposed research is the determination of the final source classification.