Improved Cosmological Constraints from New, Old and Combined Supernova Datasets

Details Improved Cosmological Constraints from New, Old and Combined Supernova Datasets Improved Cosmological Constraints from New, Old and Combined Supernova Datasets

2008-04-25

arxiv.org/abs/0804.4142v1

Astrophys.J.686:749-778,2008

Astronomy and Astrophysics

Astrophysics

49 pages, 17 figures; accepted for publication in Astrophysical Journal. For data tables, code for cosmological analysis and f

Scientific paper

10.1086/589937

We present a new compilation of Type Ia supernovae (SNe Ia), a new dataset of low-redshift nearby-Hubble-flow SNe and new analysis procedures to work with these heterogeneous compilations. This ``Union'' compilation of 414 SN Ia, which reduces to 307 SNe after selection cuts, includes the recent large samples of SNe Ia from the Supernova Legacy Survey and ESSENCE Survey, the older datasets, as well as the recently extended dataset of distant supernovae observed with HST. A single, consistent and blind analysis procedure is used for all the various SN Ia subsamples, and a new procedure is implemented that consistently weights the heterogeneous data sets and rejects outliers. We present the latest results from this Union compilation and discuss the cosmological constraints from this new compilation and its combination with other cosmological measurements (CMB and BAO). The constraint we obtain from supernovae on the dark energy density is $\Omega_\Lambda= 0.713^{+0.027}_{-0.029} (stat)}^{+0.036}_{-0.039} (sys)}$, for a flat, LCDM Universe. Assuming a constant equation of state parameter, $w$, the combined constraints from SNe, BAO and CMB give $w=-0.969^{+0.059}_{-0.063}(stat)^{+0.063}_{-0.066} (sys)$. While our results are consistent with a cosmological constant, we obtain only relatively weak constraints on a $w$ that varies with redshift. In particular, the current SN data do not yet significantly constrain $w$ at $z>1$. With the addition of our new nearby Hubble-flow SNe Ia, these resulting cosmological constraints are currently the tightest available.

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