Constraining the dark energy equation of state using Bayes theorem and the Kullback-Leibler divergence

Abstract

Data-driven model-independent reconstructions of the dark energy equation of state $w(z)$ are presented using Planck 2015 era CMB, BAO, SNIa and Lyman-$\alpha$ data. These reconstructions identify the $w(z)$ behaviour supported by the data and show a bifurcation of the equation of state posterior in the range $1.5<z<3$. Although the concordance $\Lambda$CDM model is consistent with the data at all redshifts in one of the bifurcated spaces, in the other a supernegative equation of state (also known as `phantom dark energy') is identified within the $1.5\sigma$ confidence intervals of the posterior distribution. To identify the power of different datasets in constraining the dark energy equation of state, we use a novel formulation of the Kullback--Leibler divergence. This formalism quantifies the information the data add when moving from priors to posteriors for each possible dataset combination. The SNIa and BAO datasets are shown to provide much more constraining power in comparison to the Lyman-$\alpha$ datasets. Further, SNIa and BAO constrain most strongly around redshift range $0.1-0.5$, whilst the Lyman-$\alpha$ data constrains weakly over a broader range. We do not attribute the supernegative favouring to any particular dataset, and note that the $\Lambda$CDM model was favoured at more than $2$ log-units in Bayes factors over all the models tested despite the weakly preferred $w(z)$ structure in the data.

arXiv comment9 pages, 3 tables, 5 figures; published in MNRAS

References in corpus (41)

• Planck 2015 results. XIII. Cosmological parameters
• Efficient Computation of CMB anisotropies in closed FRW models
• Cosmological Imprint of an Energy Component with General Equation of State
• Cosmological parameters from CMB and other data: a Monte-Carlo approach
• A Phantom Menace? Cosmological consequences of a dark energy component with super-negative equation of state
• Exploring the Expansion History of the Universe
• Accelerating Universes with Scaling Dark Matter
• Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples
• The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: Baryon Acoustic Oscillations in the Data Release 10 and 11 galaxy samples
• Baryon Acoustic Oscillations in the Lyα forest of BOSS DR11 quasars
• Cosmological implications of baryon acoustic oscillation (BAO) measurements
• Reconstructing Dark Energy
• Planck 2015 results. XI. CMB power spectra, likelihoods, and robustness of parameters
• Quintessence: A Review
• Quasar-Lyman $\alpha$ Forest Cross-Correlation from BOSS DR11 : Baryon Acoustic Oscillations
• Dark Matter and Dark Energy
• CMB power spectrum parameter degeneracies in the era of precision cosmology
• PolyChord: nested sampling for cosmology
• PolyChord: next-generation nested sampling
• Reconstruction of dark energy and expansion dynamics using Gaussian processes
• WMAP constraints on low redshift evolution of dark energy
• Planck 2015 results. XIV. Dark energy and modified gravity
• Parameterization of Dark-Energy Properties: a Principal-Component Approach
• Crossing the Phantom Divide with Parameterized Post-Friedmann Dark Energy
• Nonparametric Dark Energy Reconstruction from Supernova Data
• The impact of priors and observables on parameter inferences in the Constrained MSSM
• Nonparametric Reconstruction of the Dark Energy Equation of State
• Bayesian model selection without evidences: application to the dark energy equation-of-state
• Model selection applied to reconstruction of the Primordial Power Spectrum
• No Evidence for Dark Energy Dynamics from a Global Analysis of Cosmological Data
• Reconstruction of the Dark Energy equation of state
• Bayesian optimal reconstruction of the primordial power spectrum
• Information Gains from Cosmic Microwave Background Experiments
• The Knotted Sky I: Planck constraints on the primordial power spectrum
• Observational constraints on dark energy with a fast varying equation of state
• Quantifying Concordance in Cosmology
• Information Gains from Cosmological Probes
• Reconstructing the history of dark energy using maximum entropy
• Bayesian Model Averaging in Astrophysics: A Review
• High-resolution temporal constraints on the dynamics of dark energy
• Revisiting a model-independent dark energy reconstruction method

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• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
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• Exploring Cosmic Origins with CORE: Inflation
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• Dark energy in light of the early JWST observations: case for a negative cosmological constant?
• Model-independent reconstruction of the Interacting Dark Energy Kernel: Binned and Gaussian process
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• fgivenx: A Python package for functional posterior plotting
• Soundness of Dark Energy properties
• Cosmological parameter inference with Bayesian statistics
• Large-scale (in-) stability Analysis of an Exactly Solved Coupled Dark-Energy Model
• Simple-graduated dark energy and spatial curvature
• Bayesian inflationary reconstructions from Planck 2018 data
• Reconstruction of the neutrino mass as a function of redshift
• Model selection applied to reconstructions of the Dark Energy
• Label Switching Problem in Bayesian Analysis for Gravitational Wave Astronomy
• Fourier-series expansion of the dark-energy equation of state
• Neural network reconstructions for the Hubble parameter, growth rate and distance modulus
• The Einstein dark energy model
• Anisotropic massive Brans-Dicke gravity extension of the standard $\Lambda$CDM model
• Bayesian model selection on Scalar $\epsilon$-Field Dark Energy
• Observational constraints on conformal time symmetry, missing matter and double dark energy
• Coupled Multi Scalar Field Dark Energy
• Neural Networks Optimized by Genetic Algorithms in Cosmology
• Non-parametric reconstruction of cosmological observables using Gaussian Processes Regression
• Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning
• Interacting Scalar fields: Dark matter and early dark energy
• Free-form modelling of galaxy clusters: a Bayesian and data-driven approach
• FlexKnot as a Generalised Model of the Sky-averaged 21-cm Signal at z ~ 6-30 in the Presence of Systematics
• Stress testing the dark energy equation of state imprint on supernova data
• A cosmology-independent calibration of type Ia supernovae data
• Data-Space Validation of High-Dimensional Models by Comparing Sample Quantiles
• Influence of the correlation prior on reconstruction of the dark energy equation of state
• How Holographic is the Dark Energy? A Spline Nodal reconstruction approach