Comparison of Recent SnIa datasets

TL;DR

This paper evaluates and ranks six recent Type Ia supernova datasets based on their effectiveness in constraining dark energy parameters, their consistency with the cosmological constant, and their agreement with standard cosmological rulers, revealing insights into dataset reliability and internal consistency.

Contribution

It provides a comprehensive ranking of recent SnIa datasets using multiple criteria and introduces a new statistic for assessing their internal consistency.

Findings

Higher number of supernovae increases the Figure of Merit.

Standard rulers outperform supernova datasets in constraining dark energy.

Including the Gold06 dataset reduces overall internal consistency.

Abstract

We rank the six latest Type Ia supernova (SnIa) datasets (Constitution (C), Union (U), ESSENCE (Davis) (E), Gold06 (G), SNLS 1yr (S) and SDSS-II (D)) in the context of the Chevalier-Polarski-Linder (CPL) parametrization $w(a)=w_0+w_1 (1-a)$, according to their Figure of Merit (FoM), their consistency with the cosmological constant ($\Lambda$CDM), their consistency with standard rulers (Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations (BAO)) and their mutual consistency. We find a significant improvement of the FoM (defined as the inverse area of the 95.4% parameter contour) with the number of SnIa of these datasets ((C) highest FoM, (U), (G), (D), (E), (S) lowest FoM). Standard rulers (CMB+BAO) have a better FoM by about a factor of 3, compared to the highest FoM SnIa dataset (C). We also find that the ranking sequence based on consistency with $\Lambda$CDM is identical with the corresponding ranking based on consistency with standard rulers ((S) most consistent, (D), (C), (E), (U), (G) least consistent). The ranking sequence of the datasets however changes when we consider the consistency with an expansion history corresponding to evolving dark energy $(w_0,w_1)=(-1.4,2)$ crossing the phantom divide line $w=-1$ (it is practically reversed to (G), (U), (E), (S), (D), (C)). The SALT2 and MLCS2k2 fitters are also compared and some peculiar features of the SDSS-II dataset when standardized with the MLCS2k2 fitter are pointed out. Finally, we construct a statistic to estimate the internal consistency of a collection of SnIa datasets. We find that even though there is good consistency among most samples taken from the above datasets, this consistency decreases significantly when the Gold06 (G) dataset is included in the sample.