Bayesian Analysis of Inflation III: Slow Roll Reconstruction Using Model Selection

TL;DR

This paper applies Bayesian model selection to inflationary cosmology using slow roll reconstruction, constraining inflationary potentials with current cosmological data and demonstrating that only the first two slow roll parameters are necessary.

Contribution

It implements slow roll reconstruction within ModeCode and computes Bayesian evidence for inflationary models, providing updated constraints from recent cosmological datasets.

Findings

Data are well-described by the first two slow roll parameters,  and .

No evidence supports the need for a nontrivial  parameter.

The method offers an optimal inverse problem solution for inflationary potential reconstruction.

Abstract

We implement Slow Roll Reconstruction -- an optimal solution to the inverse problem for inflationary cosmology -- within ModeCode, a publicly available solver for the inflationary dynamics. We obtain up-to-date constraints on the reconstructed inflationary potential, derived from the WMAP 7-year dataset and South Pole Telescope observations, combined with large scale structure data derived from SDSS Data Release 7. Using ModeCode in conjunction with the MultiNest sampler, we compute Bayesian evidence for the reconstructed potential at each order in the truncated slow roll hierarchy. We find that the data are well-described by the first two slow roll parameters, \epsilon and \eta, and that there is no need to include a nontrivial \xi parameter.