Testing the dark energy with gravitational lensing statistics

TL;DR

This study uses gravitational lensing statistics to constrain dark energy models, providing evidence for accelerated cosmic expansion and favoring the cosmological constant over alternative models.

Contribution

It offers new constraints on dark energy parameters using lensing data and compares multiple models with Bayesian criteria, accounting for systematic uncertainties.

Findings

Supports accelerated expansion excluding null hypothesis at >4σ

Favors the cosmological constant model with minimal parameters

Systematic errors are comparable to statistical errors in current data

Abstract

We study the redshift distribution of two samples of early-type gravitational lenses, extracted from a larger collection of 122 systems, to constrain the cosmological constant in the LCDM model and the parameters of a set of alternative dark energy models (XCDM, Dvali-Gabadadze-Porrati and Ricci dark energy models), under a spatially flat universe. The likelihood is maximized for $\Omega_\Lambda= 0.70 \pm 0.09$ when considering the sample excluding the SLACS systems (known to be biased towards large image-separation lenses) and no-evolution, and $\Omega_\Lambda= 0.81\pm 0.05$ when limiting to gravitational lenses with image separation larger than 2" and no-evolution. In both cases, results accounting for galaxy evolution are consistent within 1$\sigma$. The present test supports the accelerated expansion, by excluding the null-hypothesis (i.e., $\Omega_\Lambda = 0 $) at more than 4$\sigma$, regardless of the chosen sample and assumptions on the galaxy evolution. A comparison between competitive world models is performed by means of the Bayesian information criterion. This shows that the simplest cosmological constant model - that has only one free parameter - is still preferred by the available data on the redshift distribution of gravitational lenses. We perform an analysis of the possible systematic effects, finding that the systematic errors due to sample incompleteness, galaxy evolution and model uncertainties approximately equal the statistical errors, with present-day data. We find that the largest sources of systemic errors are the dynamical normalization and the high-velocity cut-off factor, followed by the faint-end slope of the velocity dispersion function.