Testing Models of Intrinsic Brightness Variations in Type Ia Supernovae, and their Impact on Measuring Cosmological Parameters
Richard Kessler, Julien Guy, John Marriner, Marc Betoule, Jon, Brinkmann, David Cinabro, Patrick El-Hage, Joshua Frieman, Saurabh Jha,, Jennifer Mosher, Donald P. Schneider

TL;DR
This study evaluates models of intrinsic brightness variations in Type Ia supernovae, emphasizing wavelength-dependent scatter and its impact on cosmological parameter estimation, finding that proper modeling minimizes bias in dark energy measurements.
Contribution
It demonstrates that wavelength-dependent intrinsic scatter models are necessary for accurate supernova cosmology and shows how incorrect models can bias dark energy parameter estimates.
Findings
Wavelength-independent scatter models are inadequate.
Proper wavelength-dependent scatter modeling reduces bias in $w$ estimates.
Systematic uncertainties from scatter modeling are below current total uncertainties.
Abstract
For spectroscopically confirmed Type Ia supernovae we evaluate models of intrinsic brightness variations with detailed data/Monte Carlo comparisons of the dispersion in the following quantities: Hubble-diagram scatter, color difference (B-V-c) between the true B-V color and the fitted color (c) from the SALT-II light curve model, and photometric redshift residual. The data sample includes 251 ugriz light curves from the 3-season Sloan Digital Sky Survey-II, and 191 griz light curves from the Supernova Legacy Survey 3-year data release. We find that the simplest model of a wavelength-independent (coherent) scatter is not adequate, and that to describe the data the intrinsic scatter model must have wavelength-dependent variations. We use Monte Carlo simulations to examine the standard approach of adding a coherent scatter term in quadrature to the distance-modulus uncertainty in order to…
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