Crossing Statistic: Bayesian interpretation, model selection and resolving dark energy parametrization problem

Abstract

By introducing Crossing functions and hyper-parameters I show that the Bayesian interpretation of the Crossing Statistics [1] can be used trivially for the purpose of model selection among cosmological models. In this approach to falsify a cosmological model there is no need to compare it with other models or assume any particular form of parametrization for the cosmological quantities like luminosity distance, Hubble parameter or equation of state of dark energy. Instead, hyper-parameters of Crossing functions perform as discriminators between correct and wrong models. Using this approach one can falsify any assumed cosmological model without putting priors on the underlying actual model of the universe and its parameters, hence the issue of dark energy parametrization is resolved. It will be also shown that the sensitivity of the method to the intrinsic dispersion of the data is small that is another important characteristic of the method in testing cosmological models dealing with data with high uncertainties.

14 pages, 4 figures, discussions extended, 1 figure and two references added, main results unchanged, matches the final version to be published in JCAP

References in corpus (8)

• Reconstructing Dark Energy
• Two new diagnostics of dark energy
• Nonparametric Dark Energy Reconstruction from Supernova Data
• Nonparametric Reconstruction of the Dark Energy Equation of State
• A litmus test for Lambda
• Model Independent Reconstruction of the Expansion History of the Universe and the Properties of Dark Energy
• Fables of reconstruction: controlling bias in the dark energy equation of state
• Bayesian Evidence for a Cosmological Constant using new High-Redshift Supernovae Data

Cited by in corpus (27)

• Gaussian Process Cosmography
• DESI 2024: Reconstructing Dark Energy using Crossing Statistics with DESI DR1 BAO data
• Power asymmetry in WMAP and Planck temperature sky maps as measured by a local variance estimator
• Cosmographic analysis with Chebyshev polynomials
• A model-independent determination of the Hubble constant from lensed quasars and supernovae using Gaussian process regression
• Cosmological discordances II: Hubble constant, Planck and large-scale-structure data sets
• Cosmological discordances: a new measure, marginalization effects, and application to geometry vs growth current data sets
• Robust and model-independent cosmological constraints from distance measurements
• Crossing Statistic: Reconstructing the Expansion History of the Universe
• Ruling Out New Physics at Low Redshift as a solution to the $H_0$ Tension
• Exploring scalar field dynamics with Gaussian processes
• Testing Isotropic Universe Using the Gamma-Ray Burst Data of Fermi / GBM
• Model Independent Expansion History from Supernovae: Cosmology versus Systematics
• Defying the laws of Gravity I: model-independent reconstruction of the Universe expansion from growth data
• Confronting the concordance model of cosmology with Planck data
• Joint Planck and WMAP Assessment of Low CMB Multipoles
• Update on Testing the Isotropy of the Properties of Gamma-Ray Bursts
• Searching for systematics in SNIa and galaxy cluster data using the cosmic duality relation
• Testing the Distance-Duality Relation from Strong Gravitational Lensing, Type Ia Supernovae and Gamma-Ray Bursts Data up to redshift $z\sim 3.6$
• Be It Unresolved: Measuring Time Delays from Lensed Supernovae
• Consistency of the Planck CMB data and $\Lambda$CDM cosmology
• Test of consistency between Planck and WMAP
• Cosmological constraints from low-redshift data
• Subpercent Accurate Fitting of Modified Gravity Growth
• Falsifying Cosmological Constant
• Out of One, Many: Distinguishing Time Delays from Lensed Supernovae
• Star-Crossed Labours: Checking Consistency Between Current Supernovae Compilations