Model selection and parameter estimation using the iterative smoothing method

Abstract

We compute the distribution of likelihoods from the non-parametric iterative smoothing method over a set of mock Pantheon-like type Ia supernova datasets. We use this likelihood distribution to test whether typical dark energy models are consistent with the data and to perform parameter estimation. In this approach, the consistency of a model and the data is determined without the need for comparison with another alternative model. Simulating future WFIRST-like data, we study type II errors and show how confidently we can distinguish different dark energy models using this non-parametric approach.

8 pages, 6 figures, 3 tables

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Cited by in corpus (7)

• The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics
• On the evolution of the Hubble constant with the SNe Ia Pantheon Sample and Baryon Acoustic Oscillations: a feasibility study for GRB-cosmology in 2030
• DESI 2024: Reconstructing Dark Energy using Crossing Statistics with DESI DR1 BAO data
• On The Distribution of Bayesian Evidences
• Litmus tests of the flat $\Lambda$CDM model and model-independent measurement of $H_0{r}_{\mathrm{d}}$ with LSST and DESI
• Star-Crossed Labours: Checking Consistency Between Current Supernovae Compilations
• Bayesian vs Frequentist: Comparing Bayesian model selection with a frequentist approach using the iterative smoothing method