Poster abstract details

Samples of Blue Horizontal-Branch (BHB) stars selected from Pan-STARRS DR1, GALEX GUVCat, and The Dark Energy Survey, are used to model the density and age profile of the Milky Way Stellar Halo. The selection of function of BHB stars from foreground contaminants is made possible by new applications of classification-based artificial neural networks in color-color space, while density models of varying complexity are explored with a Bayesian Markov Chain Monte Carlo methodology to identify a break radius in the Milky Way Halo. The implications of this break radius on the origin of the two-component Stellar Halo are discussed, as well as the impact on cosmological models of Milky Way formation.