Supernova Constraints and Systematic Uncertainties from the First 3 Years of the Supernova Legacy Survey

TL;DR

This work presents SNLS3 cosmological results by combining SN Ia data from SNLS with external SN samples, implementing a comprehensive covariance-based treatment of systematic uncertainties that includes their impact on light-curve models. The authors constrain the dark energy equation of state $w$ in a flat universe, obtaining $w = -0.91^{+0.16}_{-0.20} ext{ (stat)}^{+0.07}_{-0.14} ext{ (sys)}$, consistent with a cosmological constant when systematics are considered. The dominant systematic source is photometric calibration, with strong emphasis on cross-calibration of nearby data to modern natural-photometric systems; several host-galaxy, Malmquist, and evolution-related effects are also incorporated via a detailed framework. The study demonstrates a robust methodology for including systematics in SN cosmology and outlines clear paths to reduce remaining uncertainties with upcoming, better-calibrated low- and intermediate-redshift samples, ultimately improving constraints on possible time variation of $w$.

Abstract

We combine high redshift Type Ia supernovae from the first 3 years of the Supernova Legacy Survey (SNLS) with other supernova (SN) samples, primarily at lower redshifts, to form a high-quality joint sample of 472 SNe (123 low-$z$, 93 SDSS, 242 SNLS, and 14 {\it Hubble Space Telescope}). SN data alone require cosmic acceleration at >99.9% confidence, including systematic effects. For the dark energy equation of state parameter (assumed constant out to at least $z=1.4$) in a flat universe, we find $w = -0.91^{+0.16}_{-0.20}(\mathrm{stat}) ^{+0.07}_{-0.14} (\mathrm{sys})$ from SNe only, consistent with a cosmological constant. Our fits include a correction for the recently discovered relationship between host-galaxy mass and SN absolute brightness. We pay particular attention to systematic uncertainties, characterizing them using a systematics covariance matrix that incorporates the redshift dependence of these effects, as well as the shape-luminosity and color-luminosity relationships. Unlike previous work, we include the effects of systematic terms on the empirical light-curve models. The total systematic uncertainty is dominated by calibration terms. We describe how the systematic uncertainties can be reduced with soon to be available improved nearby and intermediate-redshift samples, particularly those calibrated onto USNO/SDSS-like systems.

Figures (14)

• Figure 1: Redshift and color histograms of the samples used in this analysis. The left panel shows the redshift histogram after all cuts are applied. The first bin for the low-$z$ SNe contains 108 SNe. The right panel shows the color histogram before the color cut is applied (but removing known peculiar SNe as well as those with insufficient coverage), and the cut is indicated by the vertical dashed lines.
• Figure 2: Bias in $m_B$ as a function of first epoch relative to maximum after day $-15$ for HST-like data. The diamonds are the individual SNe used in this test, the red circles with errors are the values averaged in 1 day bins.
• Figure 3: Mean Malmquist bias as a function of redshift for the SDSS sample. The sharp feature at $z=0.15$ is an artifact of the discontinuous spectroscopic efficiency model of K09 and has little effect on the cosmological constraints.
• Figure 4: Statistical SN only constraints on $\Omega_m$, $w$ assuming a flat universe and constant dark energy equation of state.
• Figure 5: Hubble diagram of the combined sample. The residuals from the best fit are shown in the bottom panel.