Inflation Model Selection meets Dark Radiation

TL;DR

This paper examines how the presence of dark radiation influences inflation model selection using Bayesian analysis, revealing that dark radiation can alter the viability of certain inflationary potentials and impact the Hubble tension solution.

Contribution

It provides a comprehensive Bayesian framework to assess inflation models considering dark radiation and demonstrates its effects on model viability and the Hubble tension.

Findings

Most inflation models remain unaffected by dark radiation.

Power-law inflation is sensitive to $N_{eff}$ and can be disfavoured or favoured depending on data.

Dark radiation impacts inflation model selection and the Hubble constant tension.

Abstract

We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard $\Lambda$CDM model and an extension including dark radiation parametrised by its effective number of relativistic species $N_\mathrm{eff}$. Using a minimal dataset (Planck low-$\ell$ polarisation, temperature power spectrum and lensing reconstruction), we find that the observational status of most inflationary models is unchanged. The exceptions are potentials such as power-law inflation that predict large values for the scalar spectral index that can only be realised when $N_\mathrm{eff}$ is allowed to vary. Adding baryon acoustic oscillations data and the B-mode data from BICEP2/Keck makes power-law inflation disfavoured, while adding local measurements of the Hubble constant $H_0$ makes power-law inflation slightly favoured compared to the best single-field plateau potentials. This illustrates how the dark radiation solution to the $H_0$ tension would have deep consequences for inflation model selection.