Fitting the Constitution SNIa Data with Redshift Binned Parameterization Method

TL;DR

This paper investigates the behavior of dark energy using redshift-binned parameterizations of SNIa data, finding that models with a rapid decrease in dark energy density at certain redshifts outperform traditional models and may hint at new physics or systematic biases.

Contribution

It introduces a step function model for dark energy density that improves fit over CPL and other models, highlighting the advantage of using density over equation of state parameterization.

Findings

A step function model with a rapid decrease at z~0.331 improves fit significantly.

Piecewise constant density models outperform similar w-based models.

Dark energy density deviates from the cosmological constant at 68.3% CL.

Abstract

In this work, we explore the cosmological consequences of the recently released Constitution sample of 397 Type Ia supernovae (SNIa). By revisiting the Chevallier-Polarski-Linder (CPL) parameterization, we find that, for fitting the Constitution set alone, the behavior of dark energy (DE) significantly deviate from the cosmological constant $\Lambda$, where the equation of state (EOS) $w$ and the energy density $\rho_{\Lambda}$ of DE will rapidly decrease along with the increase of redshift $z$. Inspired by this clue, we separate the redshifts into different bins, and discuss the models of a constant $w$ or a constant $\rho_{\Lambda}$ in each bin, respectively. It is found that for fitting the Constitution set alone, $w$ and $\rho_{\Lambda}$ will also rapidly decrease along with the increase of $z$, which is consistent with the result of CPL model. Moreover, a step function model in which $\rho_{\Lambda}$ rapidly decreases at redshift $z\sim0.331$ presents a significant improvement ($\Delta \chi^{2}=-4.361$) over the CPL parameterization, and performs better than other DE models. We also plot the error bars of DE density of this model, and find that this model deviates from the cosmological constant $\Lambda$ at 68.3% confidence level (CL); this may arise from some biasing systematic errors in the handling of SNIa data, or more interestingly from the nature of DE itself. In addition, for models with same number of redshift bins, a piecewise constant $\rho_{\Lambda}$ model always performs better than a piecewise constant $w$ model; this shows the advantage of using $\rho_{\Lambda}$, instead of $w$, to probe the variation of DE.