Observational Probes of Dark Energy

The cause for the observed acceleration in the expansion of the universe is unknown, and referred to as "dark energy" for convenience. Dark energy could be an unknown energy component, or a modification of Einstein's general relativity. This dictates the measurements that are optimal in unveiling the nature of dark energy: the cosmic expansion history, and the growth history of cosmic large scale structure. I will examine Type Ia supernovae and galaxy clustering as dark energy probes, and discuss recent results and future prospects.Comment: Minor corrections. 16 pages (at the page limit), including 16 figures. Proceeding paper for an invited plenary talk at Spanish Relativity Meeting 2011 (ERE2011

Model-Independent Measurements of Cosmic Expansion and Growth at z=0.57 Using the Anisotropic Clustering of CMASS Galaxies From the Sloan Digital Sky Survey Data Release 9

We analyze the anisotropic two dimensional galaxy correlation function (2DCF) of the CMASS galaxy sample from the Sloan Digital Sky Survey Data Release 9 (DR9) of the Baryon Oscillation Spectroscopic Survey (BOSS) data. Modeling the 2DCF fully including nonlinear effects and redshift space distortions (RSD) in the scale range of 30 to 120 h^{-1}Mpc, we find H(0.57)r_s(z_d)/c=0.0444 +/- 0.0019, D_A(0.57)/r_s(z_d)=9.01 +/- 0.23, and f_g(0.57)\sigma_8(0.57)=0.474 +/- 0.075, where r_s(z_d) is the sound horizon at the drag epoch computed using a simple integral, and f_g(z) is the growth rate at redshift z, and \sigma_8(z) represents the matter power spectrum normalization on 8h^{-1}Mpc scale at z. We find that the scales larger than 120 h^{-1}Mpc are dominated by noise in the 2DCF analysis, and that the inclusion of scales 30-40 h^{-1}Mpc significantly tightens the RSD measurement. Our measurements are consistent with previous results using the same data, but have significantly better precision since we are using all the information from the 2DCF in the scale range of 30 to 120 h^{-1}Mpc. Our measurements have been marginalized over sufficiently wide priors for the relevant parameters; they can be combined with other data to probe dark energy and gravity.Comment: 8 pages, 4 figures. Slightly modified version, accepted by MNRA

Sampling the Probability Distribution of Type Ia Supernova Lightcurve Parameters in Cosmological Analysis

In order to obtain robust cosmological constraints from Type Ia supernova (SN Ia) data, we have applied Markov Chain Monte Carlo (MCMC) to SN Ia lightcurve fitting. We develop a method for sampling the resultant probability density distributions (pdf) of the SN Ia lightcuve parameters in the MCMC likelihood analysis to constrain cosmological parameters, and validate it using simulated data sets. Applying this method to the Joint Lightcurve Analysis (JLA) data set of SNe Ia, we find that sampling the SN Ia lightcurve parameter pdf's leads to cosmological parameters closer to that of a flat Universe with a cosmological constant, compared to the usual practice of using only the best fit values of the SN Ia lightcurve parameters. Our method will be useful in the use of SN Ia data for precision cosmology.Comment: 9 pages, 6 figures, 4 tables. Revised version accepted by MNRA