Future Supernovae observations as a probe of dark energy

We study the potential impact of improved future supernovae data on our understanding of the dark energy problem. We carefully examine the relative utility of different fitting functions that can be used to parameterize the dark energy models, and provide concrete reasons why a particular choice (based on a parameterization of the equation of state) is better in almost all cases. We discuss the details of a representative sample of dark energy models and show how future supernova observations could distinguish among these. As a specific example, we consider the proposed ``SNAP'' satellite which is planned to observe around 2000 supernovae. We show how a SNAP-class data set taken alone would be a powerful discriminator among a family of models that would be approximated by a constant equation of state for the most recent epoch of cosmic expansion. We show how this family includes most of the dark energy models proposed so far. We then show how an independent measurement of $\Omega_{\rm m}$ can allow SNAP to probe the evolution of the equation of state as well, allowing further discrimination among a larger class of proposed dark energy models. We study the impact of the satellite design parameters on this method to distinguish the models and compare SNAP to alternative measurements. We establish that if we exploit the full precision of SNAP it provides a very powerful probe.Comment: 29 pages, 22 figures; replaced to match version accepted for publication in PRD, section V shortend and merged into section VI; brief discussion on non-flat cosmologies adde

Optimizing the yield of Sunyaev-Zel'dovich cluster surveys

We consider the optimum depth of a cluster survey selected using the Sunyaev-Zel'dovich effect. By using simple models for the evolution of the cluster mass function and detailed modeling for a variety of observational techniques, we show that the optimum survey yield is achieved when the average size of the clusters selected is close to the size of the telescope beam. For a total power measurement, we compute the optimum noise threshold per beam as a function of the beam size and then discuss how our results can be used in more general situations. As a by-product we gain some insight into what is the most advantageous instrumental set-up. In the case of beam switching observations one is not severely limited if one manages to set the noise threshold close to the point which corresponds to the optimum yield. By defining a particular reference configuration, we show how our results can be applied to interferometer observations. Considering a variety of alternative scenarios, we discuss how robust our conclusions are to modifications in the cluster model and cosmological parameters. The precise optimum is particularly sensitive to the amplitude of fluctuations and the profile of the gas in the cluster.Comment: 16 pages, 18 figure

Constraining Dark Energy with X-ray Galaxy Clusters, Supernovae and the Cosmic Microwave Background

We present new constraints on the evolution of dark energy from an analysis of Cosmic Microwave Background, supernova and X-ray galaxy cluster data. Our analysis employs a minimum of priors and exploits the complementary nature of these data sets. We examine a series of dark energy models with up to three free parameters: the current dark energy equation of state w_0, the early time equation of state w_et and the scale factor at transition, a_t. From a combined analysis of all three data sets, assuming a constant equation of state and that the Universe is flat, we measure w_0=-1.05+0.10-0.12. Including w_et as a free parameter and allowing a_t to vary over the range 0.5<a_t<0.95 where the data sets have discriminating power, we measure w_0=-1.27+0.33-0.39 and w_et=-0.66+0.44-0.62. We find no significant evidence for evolution in the dark energy equation of state parameter with redshift. Marginal hints of evolution in the supernovae data become less significant when the cluster constraints are also included in the analysis. The complementary nature of the data sets leads to a tight constraint on the mean matter density, Omega_m and alleviates a number of other parameter degeneracies, including that between the scalar spectral index n_s, the physical baryon density Omega_bh^2 and the optical depth tau. This complementary nature also allows us to examine models in which we drop the prior on the curvature. For non-flat models with a constant equation of state, we measure w_0=-1.09+0.12-0.15 and Omega_de=0.70+-0.03. Our analysis includes spatial perturbations in the dark energy fluid, assuming a sound speed c_s^2 =1. For our most general dark energy model, not including such perturbations would lead to spurious constraints on w_et which would be tighter by approximately a factor two with the current data. (abridged)Comment: 11 pages, 13 figures, 2 tables. Accepted for publication in MNRAS. Two new figures added: Fig.9 shows the effects of including dark energy perturbations and Fig.10 compares X-ray cluster data with 2dF dat

Cluster Probes of Dark Energy Clustering

Cluster abundances are oddly insensitive to canonical early dark energy. Early dark energy with sound speed equal to the speed of light cannot be distinguished from a quintessence model with the equivalent expansion history for $z<2$ but negligible early dark energy density, despite the different early growth rate. However, cold early dark energy, with a sound speed much smaller than the speed of light, can give a detectable signature. Combining cluster abundances with cosmic microwave background power spectra can determine the early dark energy fraction to 0.3 % and distinguish a true sound speed of 0.1 from 1 at 99 % confidence. We project constraints on early dark energy from the Euclid cluster survey, as well as the Dark Energy Survey, using both current and projected Planck CMB data, and assess the impact of cluster mass systematics. We also quantify the importance of dark energy perturbations, and the role of sound speed during a crossing of $w=-1$

Constraining dark sector perturbations II: ISW and CMB lensing tomography

Any Dark Energy (DE) or Modified Gravity (MG) model that deviates from a cosmological constant requires a consistent treatment of its perturbations, which can be described in terms of an effective entropy perturbation and an anisotropic stress. We have considered a recently proposed generic parameterisation of DE/MG perturbations and compared it to data from the Planck satellite and six galaxy catalogues, including temperature-galaxy (Tg), CMB lensing-galaxy and galaxy-galaxy (gg) correlations. Combining these observables of structure formation with tests of the background expansion allows us to investigate the properties of DE/MG both at the background and the perturbative level. Our constraints on DE/MG are mostly in agreement with the cosmological constant paradigm, while we also find that the constraint on the equation of state w (assumed to be constant) depends on the model assumed for the perturbation evolution. We obtain $w=-0.92^{+0.20}_{-0.16}$ (95% CL; CMB+gg+Tg) in the entropy perturbation scenario; in the anisotropic stress case the result is $w=-0.86^{+0.17}_{-0.16}$. Including the lensing correlations shifts the results towards higher values of w. If we include a prior on the expansion history from recent Baryon Acoustic Oscillations (BAO) measurements, we find that the constraints tighten closely around $w=-1$, making it impossible to measure any DE/MG perturbation evolution parameters. If, however, upcoming observations from surveys like DES, Euclid or LSST show indications for a deviation from a cosmological constant, our formalism will be a useful tool towards model selection in the dark sector.Comment: 25 pages, 8 figures; minor update for consistency with version accepted by JCAP (13/01/2015

Feature importance for machine learning redshifts applied to SDSS galaxies

We present an analysis of importance feature selection applied to photometric redshift estimation using the machine learning architecture Decision Trees with the ensemble learning routine Adaboost (hereafter RDF). We select a list of 85 easily measured (or derived) photometric quantities (or `features') and spectroscopic redshifts for almost two million galaxies from the Sloan Digital Sky Survey Data Release 10. After identifying which features have the most predictive power, we use standard artificial Neural Networks (aNN) to show that the addition of these features, in combination with the standard magnitudes and colours, improves the machine learning redshift estimate by 18% and decreases the catastrophic outlier rate by 32%. We further compare the redshift estimate using RDF with those from two different aNNs, and with photometric redshifts available from the SDSS. We find that the RDF requires orders of magnitude less computation time than the aNNs to obtain a machine learning redshift while reducing both the catastrophic outlier rate by up to 43%, and the redshift error by up to 25%. When compared to the SDSS photometric redshifts, the RDF machine learning redshifts both decreases the standard deviation of residuals scaled by 1/(1+z) by 36% from 0.066 to 0.041, and decreases the fraction of catastrophic outliers by 57% from 2.32% to 0.99%.Comment: 10 pages, 4 figures, updated to match version accepted in MNRA