Designing and testing inflationary models with Bayesian networks

TL;DR

This paper employs Bayesian networks to model the complex parameter space of inflationary cosmology, accounting for initial conditions and couplings, and identifies the number of e-folds as a key determinant of primordial spectra.

Contribution

It introduces a hierarchical Bayesian framework to incorporate diverse inflationary parameters and their dependencies, providing a comprehensive generative model for primordial spectra.

Findings

Number of e-folds $N_*$ is the most influential parameter.

Bayesian networks effectively model dependencies among inflationary parameters.

The approach highlights the importance of initial conditions and reheating physics.

Abstract

Even simple inflationary scenarios have many free parameters. Beyond the variables appearing in the inflationary action, these include dynamical initial conditions, the number of fields, and couplings to other sectors. These quantities are often ignored but cosmological observables can depend on the unknown parameters. We use Bayesian networks to account for a large set of inflationary parameters, deriving generative models for the primordial spectra that are conditioned on a hierarchical set of prior probabilities describing the initial conditions, reheating physics, and other free parameters. We use $N_f$--quadratic inflation as an illustrative example, finding that the number of $e$-folds $N_*$ between horizon exit for the pivot scale and the end of inflation is typically the most important parameter, even when the number of fields, their masses and initial conditions are unknown, along with possible conditional dependencies between these parameters.