Model selection and parameter estimation using the iterative smoothing method

TL;DR

This paper introduces a non-parametric iterative smoothing method to evaluate the consistency of dark energy models with supernova data and to perform parameter estimation without relying on alternative models.

Contribution

It develops a novel iterative smoothing approach for model selection and parameter estimation directly from supernova datasets, bypassing traditional model comparison.

Findings

The method accurately tests model-data consistency.

Simulations show effective discrimination between dark energy models.

The approach is robust with future survey data.

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.