Bayesian Analysis of Inflation II: Model Selection and Constraints on Reheating

TL;DR

This paper employs Bayesian methods with numerical tools to evaluate inflationary models, demonstrating current data's limitations and projecting Planck's potential to constrain reheating scenarios and multi-regime inflation models.

Contribution

It introduces a Bayesian framework combining ModeCode and MultiNest for inflation model selection, emphasizing realistic priors and analyzing future data constraints.

Findings

Current data tightly constrains the power spectrum but cannot distinguish many simple inflation models.

Simulated Planck data will improve model discrimination but not fully resolve model selection.

Planck will tightly constrain models with multiple inflationary regimes and limit reheating scenarios.

Abstract

We discuss the model selection problem for inflationary cosmology. We couple ModeCode, a publicly-available numerical solver for the primordial perturbation spectra, to the nested sampler MultiNest, in order to efficiently compute Bayesian evidence. Particular attention is paid to the specification of physically realistic priors, including the parametrization of the post-inflationary expansion and associated thermalization scale. It is confirmed that while present-day data tightly constrains the properties of the power spectrum, it cannot usefully distinguish between the members of a large class of simple inflationary models. We also compute evidence using a simulated Planck likelihood, showing that while Planck will have more power than WMAP to discriminate between inflationary models, it will not definitively address the inflationary model selection problem on its own. However, Planck will place very tight constraints on any model with more than one observationally-distinct inflationary regime -- e.g. the large- and small-field limits of the hilltop inflation model -- and put useful limits on different reheating scenarios for a given model.