Present and future evidence for evolving dark energy

We compute the Bayesian evidences for one- and two-parameter models of evolving dark energy, and compare them to the evidence for a cosmological constant, using current data from Type Ia supernova, baryon acoustic oscillations, and the cosmic microwave background. We use only distance information, ignoring dark energy perturbations. We find that, under various priors on the dark energy parameters, LambdaCDM is currently favoured as compared to the dark energy models. We consider the parameter constraints that arise under Bayesian model averaging, and discuss the implication of our results for future dark energy projects seeking to detect dark energy evolution. The model selection approach complements and extends the figure-of-merit approach of the Dark Energy Task Force in assessing future experiments, and suggests a significantly-modified interpretation of that statistic.

[1]  P. A. P. Moran,et al.  Theory of Probability.@@@An Introduction to Probability Theory.@@@The Analysis of Time Series: An Introduction. , 1985 .

[2]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[3]  Robert B. Ash,et al.  Information Theory , 2020, The SAGE International Encyclopedia of Mass Media and Society.

[4]  Model selection as a science driver for dark energy surveys , 2005, astro-ph/0512484.

[5]  Philip C. Gregory,et al.  Bayesian Logical Data Analysis for the Physical Sciences: Acknowledgements , 2005 .

[6]  Optimizing cosmological surveys in a crowded market , 2004, astro-ph/0407201.

[7]  D. Parkinson,et al.  A Nested Sampling Algorithm for Cosmological Model Selection , 2005, astro-ph/0508461.

[8]  Yun Wang Observational signatures of the weak lensing magnification of supernovae , 2004, astro-ph/0406635.

[9]  Measuring the metric: A parametrized post-Friedmannian approach to the cosmic dark energy problem , 2001, astro-ph/0101354.

[10]  U. von Toussaint,et al.  Bayesian inference and maximum entropy methods in science and engineering , 2004 .

[11]  The cosmological constant problem and quintessence , 2002, astro-ph/0202076.

[12]  Probing Newton's constant on vast scales: Dvali-Gabadadze-Porrati gravity, cosmic acceleration, and large scale structure , 2004, astro-ph/0401515.

[13]  M. Chevallier,et al.  ACCELERATING UNIVERSES WITH SCALING DARK MATTER , 2000, gr-qc/0009008.

[14]  T. G. Barnes,et al.  A Bayesian Analysis of the Cepheid Distance Scale , 2003, astro-ph/0303656.

[15]  S. Bridle,et al.  Revealing the Nature of Dark Energy Using Bayesian Evidence , 2003, astro-ph/0305526.

[16]  D. Parkinson,et al.  Bayesian model selection analysis of WMAP3 , 2006, astro-ph/0605003.

[17]  M. Kunz,et al.  Measuring the effective complexity of cosmological models , 2006, astro-ph/0602378.

[18]  D. Huterer,et al.  How many dark energy parameters , 2005, astro-ph/0505330.

[19]  Wlodzimierz Godlowski,et al.  Which cosmological model—with dark energy or modified FRW dynamics? , 2006 .

[20]  Designer Cosmology , 2004, astro-ph/0409266.

[21]  Michael S. Turner,et al.  PROBING DARK ENERGY: METHODS AND STRATEGIES , 2000, astro-ph/0012510.

[22]  R. Nichol,et al.  Detection of the Baryon Acoustic Peak in the Large-Scale Correlation Function of SDSS Luminous Red Galaxies , 2005, astro-ph/0501171.

[23]  New dark energy constraints from supernovae, microwave background, and galaxy clustering. , 2004, Physical review letters.

[24]  J. Berger,et al.  Erratum: “A Bayesian Analysis of the Cepheid Distance Scale” (ApJ, 592, 539 [2003]) , 2004 .

[25]  Top ten accelerating cosmological models , 2006, astro-ph/0604327.

[26]  L. Goddard Information Theory , 1962, Nature.

[27]  P. Peebles,et al.  The Cosmological Constant and Dark Energy , 2002, astro-ph/0207347.

[28]  A. Hense,et al.  A Bayesian approach to climate model evaluation and multi‐model averaging with an application to global mean surface temperatures from IPCC AR4 coupled climate models , 2006 .

[29]  P. Gregory Bayesian Logical Data Analysis for the Physical Sciences: The how-to of Bayesian inference , 2005 .

[30]  T. Padmanabhan Cosmological constant—the weight of the vacuum , 2002, hep-th/0212290.

[31]  P. Peebles,et al.  Cosmology with a Time Variable Cosmological Constant , 1988 .

[32]  Eric V. Linder,et al.  Cosmic growth history and expansion history , 2005 .

[33]  R. Bacon,et al.  Overview of the Nearby Supernova Factory , 2002, SPIE Astronomical Telescopes + Instrumentation.

[34]  Bayesian model selection and isocurvature perturbations , 2005, astro-ph/0501477.

[35]  D. Parkinson,et al.  Model selection forecasts for the spectral index from the Planck satellite , 2006, astro-ph/0605004.

[36]  L. M. M.-T. Theory of Probability , 1929, Nature.

[37]  Stefano Casertano,et al.  Type Ia Supernova Discoveries at z > 1 from the Hubble Space Telescope: Evidence for Past Deceleration and Constraints on Dark Energy Evolution , 2004, astro-ph/0402512.

[38]  Mark Trodden,et al.  Is Cosmic Speed-Up Due to New Gravitational Physics? , 2003, astro-ph/0306438.

[39]  Pia Mukherjee,et al.  Model-independent Constraints on Dark Energy Density from Flux-averaging Analysis of Type Ia Supernova Data , 2004 .

[40]  A. Slosar,et al.  Bayesian joint analysis of cluster weak lensing and Sunyaev–Zel'dovich effect data , 2003, astro-ph/0307098.

[41]  Pia Mukherjee,et al.  Robust Dark Energy Constraints from Supernovae, Galaxy Clustering, and 3 yr Wilkinson Microwave Anisotropy Probe Observations , 2006, astro-ph/0604051.

[42]  C. Wetterich COSMOLOGY AND THE FATE OF DILATATION SYMMETRY , 1988, 1711.03844.

[43]  A. Lewis,et al.  Cosmological parameters from CMB and other data: A Monte Carlo approach , 2002, astro-ph/0205436.

[44]  I. J. Good,et al.  Theory of Probability Harold Jeffreys (Third edition, 447 + ix pp., Oxford Univ. Press, 84s.) , 1962 .

[45]  The Essence of Quintessence and the Cost of Compression , 2004, astro-ph/0407364.