Challenges in the hunt for dark energy dynamics

Includes bibliographical references. .One of the greatest challenges in modern cosmology is determining the origin of the observed acceleration of the Universe. The 'dark energy' believed to supply the negative pressure responsible for this cosmic acceleration remains elusive despite over a decade of investigation. Hunting for deviation from the 'vanilla' cosmological model, ACDM, and detecting dynamics with redshift in the equation of state remains a key research area, with many challenges. We introduce some of the challenges in the search for such dark energy dynamics. We illustrate that under the assumption of well-motivated scaling models for dark energy dynamics early universe constraints on the dark energy density imply that these models will be essentially indistinguishable from ACDM for the next decade. After introducing the Fisher Matrix formalism, we derive the Fisher Flex test as a measure of whether the assumption of Gaussianity in the likelihood is incorrect for parameter estimation. This formalism is general for any cosmological survey. Lastly, we study the degeneracies between dark energy and curvature and matter in a non-parametric approach, and show that incorrectly assuming values of cosmological components can exactly mimic dark energy dynamics. We connect to the parametric approach by showing how these uncertainties also degrade constraints on the dark energy parameters in an assumed functional form for w. Improving the accuracy of surveys and experiments to search for possible signatures of dark energy dynamics is the focus of much attention in contemporary cosmology; we highlight challenges in the hunt for dark energy dynamics

Extending BEAMS to incorporate correlated systematic uncertainties

New supernova surveys such as the Dark Energy Survey, Pan-STARRS and the LSST will produce an unprecedented number of photometric supernova candidates, most with no spectroscopic data. Avoiding biases in cosmological parameters due to the resulting inevitable contamination from non-Ia supernovae can be achieved with the BEAMS formalism, allowing for fully photometric supernova cosmology studies. Here we extend BEAMS to deal with the case in which the supernovae are correlated by systematic uncertainties. The analytical form of the full BEAMS posterior requires evaluating 2^N terms, where N is the number of supernova candidates. This `exponential catastrophe' is computationally unfeasible even for N of order 100. We circumvent the exponential catastrophe by marginalising numerically instead of analytically over the possible supernova types: we augment the cosmological parameters with nuisance parameters describing the covariance matrix and the types of all the supernovae, \tau_i, that we include in our MCMC analysis. We show that this method deals well even with large, unknown systematic uncertainties without a major increase in computational time, whereas ignoring the correlations can lead to significant biases and incorrect credible contours. We then compare the numerical marginalisation technique with a perturbative expansion of the posterior based on the insight that future surveys will have exquisite light curves and hence the probability that a given candidate is a Type Ia will be close to unity or zero, for most objects. Although this perturbative approach changes computation of the posterior from a 2^N problem into an N^2 or N^3 one, we show that it leads to biases in general through a small number of misclassifications, implying that numerical marginalisation is superior.Comment: Resubmitted under married name Lochner (formally Knights). Version 3: major changes, including a large scale analysis with thousands of MCMC chains. Matches version published in JCAP. 23 pages, 8 figure

Towards the Future of Supernova Cosmology

For future surveys, spectroscopic follow-up for all supernovae will be extremely difficult. However, one can use light curve fitters, to obtain the probability that an object is a Type Ia. One may consider applying a probability cut to the data, but we show that the resulting non-Ia contamination can lead to biases in the estimation of cosmological parameters. A different method, which allows the use of the full dataset and results in unbiased cosmological parameter estimation, is Bayesian Estimation Applied to Multiple Species (BEAMS). BEAMS is a Bayesian approach to the problem which includes the uncertainty in the types in the evaluation of the posterior. Here we outline the theory of BEAMS and demonstrate its effectiveness using both simulated datasets and SDSS-II data. We also show that it is possible to use BEAMS if the data are correlated, by introducing a numerical marginalisation over the types of the objects. This is largely a pedagogical introduction to BEAMS with references to the main BEAMS papers.Comment: Replaced under married name Lochner (formally Knights). 3 pages, 2 figures. To appear in the Proceedings of 13th Marcel Grossmann Meeting (MG13), Stockholm, Sweden, 1-7 July 201

Bayesian estimation applied to multiple species

Observed data are often contaminated by undiscovered interlopers, leading to biased parameter estimation. Here we present BEAMS (Bayesian estimation applied to multiple species) which significantly improves on the standard maximum likelihood approach in the case where the probability for each data point being “pure” is known. We discuss the application of BEAMS to future type-Ia supernovae (SNIa) surveys, such as LSST, which are projected to deliver over a million supernovae light curves without spectra. The multiband light curves for each candidate will provide a probability of being Ia (pure) but the full sample will be significantly contaminated with other types of supernovae and transients. Given a sample of N supernovae with mean probability, ⟨P⟩, of being Ia, BEAMS delivers parameter constraints equal to N⟨P⟩ spectroscopically confirmed SNIa. In addition BEAMS can be simultaneously used to tease apart different families of data and to recover properties of the underlying distributions of those families (e.g. the type-Ibc and II distributions). Hence BEAMS provides a unified classification and parameter estimation methodology which may be useful in a diverse range of problems such as photometric redshift estimation or, indeed, any parameter estimation problem where contamination is an issue

A search for ultra-light axions using precision cosmological data

Ultra-light axions (ULAs) with masses in the range 10^{-33} eV <m <10^{-20} eV are motivated by string theory and might contribute to either the dark-matter or dark-energy density of the Universe. ULAs could suppress the growth of structure on small scales, or lead to an enhanced integrated Sachs-Wolfe effect on large-scale cosmic microwave-background (CMB) anisotropies. In this work, cosmological observables over the full ULA mass range are computed, and then used to search for evidence of ULAs using CMB data from the Wilkinson Microwave Anisotropy Probe (WMAP), Planck satellite, Atacama Cosmology Telescope, and South Pole Telescope, as well as galaxy clustering data from the WiggleZ galaxy-redshift survey. In the mass range 10^{-32} eV < m <10^{-25.5} eV, the axion relic-density \Omega_{a} (relative to the total dark-matter relic density \Omega_{d}) must obey the constraints \Omega_{a}/\Omega_{d} < 0.05 and \Omega_{a}h^{2} < 0.006 at 95%-confidence. For m> 10^{-24} eV, ULAs are indistinguishable from standard cold dark matter on the length scales probed, and are thus allowed by these data. For m < 10^{-32} eV, ULAs are allowed to compose a significant fraction of the dark energy.Comment: 31 pages, 16 figures, 1 table, updated to have same figure line-types/language as version published in Phys. Rev. D, grammatical corrections made, references added, results unchange

Axiverse cosmology and the energy scale of inflation

Ultra-light axions ($m_a\lesssim 10^{-18}$eV), motivated by string theory, can be a powerful probe of the energy scale of inflation. In contrast to heavier axions the isocurvature modes in the ultra-light axions can coexist with observable gravitational waves. Here it is shown that large scale structure constraints severely limit the parameter space for axion mass, density fraction and isocurvature amplitude. It is also shown that radically different CMB observables for the ultra-light axion isocurvature mode additionally reduce this space. The results of a new, accurate and efficient method to calculate this isocurvature power spectrum are presented, and can be used to constrain ultra-light axions and inflation.Comment: 4 pages, 3 figures, v3 some references added, matches version published in Physical Review

Characterizing the contaminating distance distribution for Bayesian supernova cosmology

Measurements of the equation of state of dark energy from surveys of thousands of Type Ia Supernovae (SNe Ia) will be limited by spectroscopic follow-up and must therefore rely on photometric identification, increasing the chance that the sample is contaminated by Core Collapse Supernovae (CC SNe). Bayesian methods for supernova cosmology can remove contamination bias while maintaining high statistical precision but are sensitive to the choice of parameterization of the contaminating distance distribution. We use simulations to investigate the form of the contaminating distribution and its dependence on the absolute magnitudes, light curve shapes, colors, extinction, and redshifts of core collapse supernovae. We find that the CC luminosity function dominates the distance distribution function, but its shape is increasingly distorted as the redshift increases and more CC SNe fall below the survey magnitude limit. The shapes and colors of the CC light curves generally shift the distance distribution, and their effect on the CC distances is correlated. We compare the simulated distances to the first year results of the SDSS-II SN survey and find that the SDSS distance distributions can be reproduced with simulated CC SNe that are ~1 mag fainter than the standard Richardson et al. (2002) luminosity functions, which do not produce a good fit. To exploit the full power of the Bayesian parameter estimation method, parameterization of the contaminating distribution should be guided by the current knowledge of the CC luminosity functions, coupled with the effects of the survey selection and magnitude-limit, and allow for systematic shifts caused by the parameters of the distance fit.Comment: 17 pages, 5 figures; accepted for publication in the Astrophysical Journa

Photometric Supernova Cosmology with BEAMS and SDSS-II

Supernova cosmology without spectroscopic confirmation is an exciting new frontier which we address here with the Bayesian Estimation Applied to Multiple Species (BEAMS) algorithm and the full three years of data from the Sloan Digital Sky Survey II Supernova Survey (SDSS-II SN). BEAMS is a Bayesian framework for using data from multiple species in statistical inference when one has the probability that each data point belongs to a given species, corresponding in this context to different types of supernovae with their probabilities derived from their multi-band lightcurves. We run the BEAMS algorithm on both Gaussian and more realistic SNANA simulations with of order 10^4 supernovae, testing the algorithm against various pitfalls one might expect in the new and somewhat uncharted territory of photometric supernova cosmology. We compare the performance of BEAMS to that of both mock spectroscopic surveys and photometric samples which have been cut using typical selection criteria. The latter typically are either biased due to contamination or have significantly larger contours in the cosmological parameters due to small data-sets. We then apply BEAMS to the 792 SDSS-II photometric supernovae with host spectroscopic redshifts. In this case, BEAMS reduces the area of the (\Omega_m,\Omega_\Lambda) contours by a factor of three relative to the case where only spectroscopically confirmed data are used (297 supernovae). In the case of flatness, the constraints obtained on the matter density applying BEAMS to the photometric SDSS-II data are \Omega_m(BEAMS)=0.194\pm0.07. This illustrates the potential power of BEAMS for future large photometric supernova surveys such as LSST.Comment: 25 pages, 15 figures, submitted to Ap

Power-law Template for Infrared Point-source Clustering

We perform a combined fit to angular power spectra of unresolved infrared (IR) point sources from the Planck satellite (at 217, 353, 545, and 857 GHz, over angular scales 100 ≾ ℓ ≾ 2200), the Balloon-borne Large-Aperture Submillimeter Telescope (BLAST; 250, 350, and 500μm; 1000 ≾ ℓ ≾ 9000), and from correlating BLAST and Atacama Cosmology Telescope (ACT; 148 and 218 GHz) maps. We find that the clustered power over the range of angular scales and frequencies considered is well fitted by a simple power law of the form C^(clust)_ℓ ∝ ℓ^(-n) with n = 1.25 ± 0.06. While the IR sources are understood to lie at a range of redshifts, with a variety of dust properties, we find that the frequency dependence of the clustering power can be described by the square of a modified blackbody, ν^(β)B(ν, T_(eff)), with a single emissivity index β = 2.20 ± 0.07 and effective temperature T_(eff) = 9.7 K. Our predictions for the clustering amplitude are consistent with existing ACT and South Pole Telescope results at around 150 and 220 GHz, as is our prediction for the effective dust spectral index, which we find to be α_(150–220) = 3.68±0.07 between 150 and 220 GHz. Our constraints on the clustering shape and frequency dependence can be used to model the IR clustering as a contaminant in cosmic microwave background anisotropy measurements. The combined Planck and BLAST data also rule out a linear bias clustering model