Optimising Inflationary Features the Bayesian Way

TL;DR

This paper introduces BayOp, a Bayesian optimisation-based method for efficient likelihood sampling, applied to cosmological data analysis, demonstrating its effectiveness in exploring complex likelihood landscapes without finding new inflationary features.

Contribution

The paper presents BayOp, a novel Bayesian optimisation approach for likelihood sampling, improving efficiency in analyzing complex cosmological models compared to traditional methods.

Findings

BayOp efficiently samples likelihoods in complex parameter spaces.

No new inflationary features were detected in Planck data.

BayOp outperforms traditional sampling methods in speed and efficiency.

Abstract

Modern cosmological data demand modern data analysis techniques. We introduce BayOp, a new likelihood sampling and maximisation method which is based on the Bayesian Optimisation algorithm and learns a function instead of randomly sampling from it. We apply BayOp to analyse Planck data for traces of inflationary features models with global periodic modulations of the primordial power spectrum. While we do not find any new evidence for features, we demonstrate that BayOp provides an extremely efficient way of sampling likelihoods over low-to-moderate-dimensional parameter spaces, even for very complex likelihood landscapes.