Dark Energy Constraints from the CTIO Lensing Survey

TL;DR

This study analyzes the 75 square-degree CTIO weak-lensing survey in combination with CMB and Type Ia SN data to constrain cosmological parameters and the dark-energy equation of state. Using aperture-mass and shear-variance statistics, together with a nonlinear power-spectrum model and WMAP priors, the authors quantify constraints on $\Omega_m$, $\sigma_8$, and $w$ under ΛCDM, constant-$w$, and time-varying-$w$ scenarios. They find results broadly consistent with ΛCDM ($\Omega_m\approx 0.26$, $\sigma_8\approx 0.82$; $w\approx -1$) with improved precision when lensing data are combined with CMB and SN, while allowing $w$ to evolve weakens constraints on some parameters. The paper also thoroughly assesses systematic uncertainties and outlines future enhancements such as tomography and higher-order lensing statistics to further tighten dark-energy constraints.

Abstract

We perform a cosmological parameter analysis of the 75 square degree CTIO lensing survey in conjunction with CMB and Type Ia supernovae data. For Lambda CDM cosmologies, we find that the amplitude of the power spectrum at low redshift is given by sigma_8 = 0.81 (+0.15,-0.10, 95% c.l.), where the error bar includes both statistical and systematic errors. The total of all systematic errors is smaller than the statistical errors, but they do make up a significant fraction of the error budget. We find that weak lensing improves the constraints on dark energy as well. The (constant) dark energy equation of state parameter, w, is measured to be -0.89 (+0.16,-0.21, 95% c.l.). Marginalizing over a constant $w$ slightly changes the estimate of sigma_8 to 0.79 (+0.17, -0.14, 95% c.l.). We also investigate variable w cosmologies, but find that the constraints weaken considerably; the next generation surveys are needed to obtain meaningful constraints on the possible time evolution of dark energy.

Figures (4)

• Figure 1: The aperture mass (left) and shear variance (right) measurements for our CTIO survey data. For the left plot, the blue (upper) points are the $E$-mode measurements, $\langle M_{\rm ap}^2 \rangle(R)$ and the red (lower) points are the $B$-mode contamination, $\langle M_\times^2 \rangle(R)$. For the right plot, the blue points are the total shear variance, including any potential $B$-mode contamination. In both cases, the black curve is the best fit flat $\Lambda$CDM model found in §\ref{['analysissection']}.
• Figure 2: Contour plots of $\chi^2$ for the $(\hbox{$\Omega_{\rm m}$},\sigma_8)$ plane. The left plot shows the effect of adding the data sets sequentially, starting with the CMB constraints, then adding the supernova and lensing data. The right plot shows contours for each of the three data sets separately. In each case the contours enclose the 68% and 95% confidence regions. The $\times$ is the best fit model. All other parameters are marginalized over as discussed in the text.
• Figure 3: Contour plots of $\chi^2$ for the $(\hbox{$\Omega_{\rm m}$},\sigma_8)$ plane (left) and the $(\hbox{$\Omega_{\rm de}$},w)$ plane (right) for the constant $w$ dark energy models. Both plots show the effect of adding the data sets sequentially. In each case the contours enclose the 68% and 95% confidence regions. The black $\times$'s are the best fit models in each plane. The cyan $\times$ in the left plot is the best fit from the $\Lambda$CDM prior (Figure \ref{['lambda_plots']}).
• Figure 4: Contour plots of $\chi^2$ for the $(\hbox{$\Omega_{\rm m}$},\sigma_8)$ plane (left) and the $(w_0,w_a)$ plane (right) for the variable $w$ dark energy models. Both plots show the effect of adding the data sets sequentially. In each case the contours enclose the 68% and 95% confidence regions. The black $\times$'s are the best fit models in each plane. The cyan $\times$'s in the left plot are the best fits from the $\Lambda$CDM and constant $w$ priors (Figures \ref{['lambda_plots']} and \ref{['w3_plots']}).