http://arxiv.org/abs/1507.01602
While recent supernova cosmology research has benefited from improved measurements, current analysis approaches are not statistically optimal and will prove insufficient for future surveys. This paper discusses the limitations of current supernova cosmological analyses in treating outliers, selection effects, shape- and color-standardization relations, intrinsic dispersion, and heterogeneous observations. We present a new Bayesian framework, called UNITY (Unified Nonlinear Inference for Type-Ia cosmologY), that incorporates significant improvements in our ability to confront these effects. We apply the framework to real supernova observations and demonstrate smaller statistical and systematic uncertainties. We verify earlier results that SNe Ia require nonlinear shape and color standardizations, but we now include these nonlinear relations in a statistically well-justified way. This analysis was blinded, in that the method was first validated on simulated data, and no analysis changes were made after transitioning to real data. We discuss possible extensions of the method.
D. Rubin, G. Aldering, K. Barbary, et. al.
Wed, 8 Jul 15
7/42
Comments: 16 pages, submitted to ApJ
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