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First-year Sloan Digital Sky Survey-II (SDSS-II) Supernova Results: Hubble Diagram and Cosmological Parameters

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arxiv 0908.4274 v1 pith:FL6S2WIJ submitted 2009-08-28 astro-ph.CO

classification astro-ph.CO
keywords measurementshubblesdss-iistatsystcosmologicaldiagramfind
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abstract

We present measurements of the Hubble diagram for 103 Type Ia supernovae (SNe) with redshifts 0.04 < z < 0.42, discovered during the first season (Fall 2005) of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey. These data fill in the redshift "desert" between low- and high-redshift SN Ia surveys. We combine the SDSS-II measurements with new distance estimates for published SN data from the ESSENCE survey, the Supernova Legacy Survey, the Hubble Space Telescope, and a compilation of nearby SN Ia measurements. Combining the SN Hubble diagram with measurements of Baryon Acoustic Oscillations from the SDSS Luminous Red Galaxy sample and with CMB temperature anisotropy measurements from WMAP, we estimate the cosmological parameters w and Omega_M, assuming a spatially flat cosmological model (FwCDM) with constant dark energy equation of state parameter, w. For the FwCDM model and the combined sample of 288 SNe Ia, we find w = -0.76 +- 0.07(stat) +- 0.11(syst), Omega_M = 0.306 +- 0.019(stat) +- 0.023(syst) using MLCS2k2 and w = -0.96 +- 0.06(stat) +- 0.12(syst), Omega_M = 0.265 +- 0.016(stat) +- 0.025(syst) using the SALT-II fitter. We trace the discrepancy between these results to a difference in the rest-frame UV model combined with a different luminosity correction from color variations; these differences mostly affect the distance estimates for the SNLS and HST supernovae. We present detailed discussions of systematic errors for both light-curve methods and find that they both show data-model discrepancies in rest-frame $U$-band. For the SALT-II approach, we also see strong evidence for redshift-dependence of the color-luminosity parameter (beta). Restricting the analysis to the 136 SNe Ia in the Nearby+SDSS-II samples, we find much better agreement between the two analysis methods but with larger uncertainties.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On the cosmological performance of photometrically classified supernovae with machine learning

    astro-ph.CO 2019-08 conditional novelty 5.0 of 10

    Machine-learning classification of simulated photometric supernovae retains up to 75 percent of cosmological information with SALT2 features and roughly one third with Newling or wavelet features.

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