Bright High z SnIa: A Challenge for LCDM?
L. Perivolaropoulos, A. Shafieloo

TL;DR
This paper introduces a new statistical method to analyze high-redshift Type Ia Supernova data, revealing an unexpected brightness trend that challenges the standard LCDM cosmological model.
Contribution
It develops a novel binning-based statistic to detect systematic brightness trends in supernova data and assesses their significance against Monte Carlo simulations within different cosmological models.
Findings
High-redshift supernova brightness data are inconsistent with LCDM expectations.
The probability of observed brightness bias occurring in LCDM is less than 6%.
Alternative models with w_0=-1.4, w_1=2 fit the data better.
Abstract
It has recently been pointed out by Kowalski et. al. (arxiv:0804.4142) that there is `an unexpected brightness of the SnIa data at z>1'. We quantify this statement by constructing a new statistic which is applicable directly on the Type Ia Supernova (SnIa) distance moduli. This statistic is designed to pick up systematic brightness trends of SnIa datapoints with respect to a best fit cosmological model at high redshifts. It is based on binning the normalized differences between the SnIa distance moduli and the corresponding best fit values in the context of a specific cosmological model (eg LCDM). We then focus on the highest redshift bin and extend its size towards lower redshifts until the Binned Normalized Difference (BND) changes sign (crosses 0) at a redshift z_c (bin size N_c). The bin size N_c of this crossing (the statistical variable) is then compared with the corresponding…
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