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Wide field surveys will soon be discovering Type Ia supernovae (SNe) at rates of several thousand per year. Spectroscopic follow-up can only scratch the surface for such enormous samples, so these extensive data sets will only be useful to the extent that they can be characterized by the survey photometry alone. In a companion paper (Rodney and Tonry, 2009) we introduced the SOFT method for analyzing SNe using direct comparison to template light curves, and demonstrated its application for photometric SN classification. In this work we extend the SOFT method to derive estimates of redshift and luminosity distance for Type Ia SNe, using light curves from the SDSS and SNLS surveys as a validation set. Redshifts determined by SOFT using light curves alone are consistent with spectroscopic redshifts, showing a root-mean-square scatter in the residuals of RMS_z=0.051. SOFT can also derive simultaneous redshift and distance estimates, yielding results that are consistent with the currently favored Lambda-CDM cosmological model. When SOFT is given spectroscopic information for SN classification and redshift priors, the RMS scatter in Hubble diagram residuals is 0.18 mags for the SDSS data and 0.28 mags for the SNLS objects. Without access to any spectroscopic information, and even without any redshift priors from host galaxy photometry, SOFT can still measure reliable redshifts and distances, with an increase in the Hubble residuals to 0.37 mags for the combined SDSS and SNLS data set. Using Monte Carlo simulations we predict that SOFT will be able to improve constraints on time-variable dark energy models by a factor of 2-3 with each new generation of large-scale SN surveys.
We use simulated SN Ia samples, including both photometry and spectra, to perform the first direct validation of cosmology analysis using the SALT-II light curve model. This validation includes residuals from the light curve training process, systema
By using the Sloan Digital Sky Survey (SDSS) first year type Ia supernova (SN Ia) compilation, we compare two different approaches (traditional chi^2 and complete likelihood) to determine parameter constraints when the magnitude dispersion is to be e
Cosmological parameter estimation is entering a new era. Large collaborations need to coordinate high-stakes analyses using multiple methods; furthermore such analyses have grown in complexity due to sophisticated models of cosmology and systematic u
Cosmological large-scale structure analyses based on two-point correlation functions often assume a Gaussian likelihood function with a fixed covariance matrix. We study the impact on cosmological parameter estimation of ignoring the parameter depend
The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics. This is often a difficult task given that the observed data can have a very high number of dimensions. In order to address this pro