Color Dispersion and Milky Way Reddening Among Type Ia Supernovae

TL;DR

This paper investigates the impact of intrinsic color dispersion on Type Ia supernovae distance measurements, revealing that proper modeling of color effects aligns data with Milky Way dust laws and reduces host galaxy correlations.

Contribution

It demonstrates that intrinsic color dispersion significantly biases supernova cosmology analyses and shows that a Milky Way reddening law better explains observed trends.

Findings

Bias in color-luminosity relation beta of -1.0 (~33%)

Positive bias in equation of state parameter w of +0.04 (~4%)

Accounting for color reduces host galaxy property correlations by ~20%

Abstract

Past analyses of Type Ia Supernovae (SNe Ia) have identified an irreducible scatter of 5-10% in distance widely attributed to an intrinsic dispersion in luminosity. Another, equally valid, source of this scatter is intrinsic dispersion in color. Misidentification of the true source of this scatter can bias both the retrieved color-luminosity relation and cosmological parameter measurements. The size of this bias depends on the magnitude of the intrinsic color dispersion relative to the distribution of colors that correlate with distance. We produce a realistic simulation of a misattribution of intrinsic scatter, and find a negative bias in the recovered color-luminosity relation, beta, of dbeta -1.0 (~33%) and a positive bias in the equation of state parameter, w, of dw +0.04 (~4%). We re-analyze current published data sets with the assumptions that the distance scatter is predominantly the result of color. Unlike previous analyses, we find that the data are consistent with a Milky Way reddening law R_V=3.1, and that a Milky Way dust model better predicts the asymmetric color-luminosity trends than the conventional luminosity scatter hypothesis. We also determine that accounting for color variation reduces the correlation between various Host galaxy properties and Hubble residuals by ~20%.