Designing and testing inflationary models with Bayesian networks

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 $N_{\ast }$ 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.

24 pages, 9 figures, 1 table; discussion updated

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Cited by in corpus (14)

• The Spectrum of the Axion Dark Sector
• Information Gain on Reheating: the One Bit Milestone
• Squeezed Bispectrum in the $\delta N$ Formalism: Local Observer Effect in Field Space
• Shortcomings of New Parametrizations of Inflation
• Manyfield Inflation in Random Potentials
• Current observations with a decaying cosmological constant allow for chaotic cyclic cosmology
• Seven Lessons from Manyfield Inflation in Random Potentials
• Black hole accretion discs and screened scalar hair
• Effect of reheating on predictions following multiple-field inflation
• Curvature Perturbations From Stochastic Particle Production During Inflation
• Inflation as an Information Bottleneck - A strategy for identifying universality classes and making robust predictions
• Expectations for Inflationary Observables: Simple or Natural?
• Clocking the End of Cosmic Inflation
• The Large Number Limit of Multifield Inflation