Two model-independent methods applied to latest SN and BAO data find the cosmic distance duality relation consistent with observations within 1 sigma and no evidence of violation.
Optimising Gaussian processes for reconstructing dark energy dynamics from supernovae
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Gaussian processes are a fully Bayesian smoothing technique that allows for the reconstruction of a function and its derivatives directly from observational data, without assuming a specific model or choosing a parameterization. This is ideal for constraining dark energy because physical models are generally phenomenological and poorly motivated. Model-independent constraints on dark energy are an especially important alternative to parameterized models, as the priors involved have an entirely different source so can be used to check constraints formulated from models or parameterizations. A critical prior for Gaussian process reconstruction lies in the choice of covariance function. We show how the choice of covariance function affects the result of the reconstruction, and present a choice which leads to reliable results for present day supernovae data. We also introduce a method to quantify deviations of a model from the Gaussian process reconstructions.
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Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
Model-independent Gaussian Process reconstruction from CC+DESI+supernova data shows positive entropy production and approach to thermodynamic equilibrium, with dark energy equation of state consistent with a cosmological constant.
citing papers explorer
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Model-independent test of the cosmic distance duality relation with recent observational data
Two model-independent methods applied to latest SN and BAO data find the cosmic distance duality relation consistent with observations within 1 sigma and no evidence of violation.
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Model-independent calibration of Gamma-Ray Bursts with neural networks
Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
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Model-independent reconstruction of cosmic thermodynamics and dark energy dynamics
Model-independent Gaussian Process reconstruction from CC+DESI+supernova data shows positive entropy production and approach to thermodynamic equilibrium, with dark energy equation of state consistent with a cosmological constant.