UNITY: Confronting Supernova Cosmology's Statistical and Systematic
  Uncertainties in a Unified Bayesian Framework
  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, unexplained
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 primarily performed blinded, in that the basic framework was first
validated on simulated data before transitioning to real data. We also discuss
possible extensions of the method.
