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Optimising Inflationary Features the Bayesian Way
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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.
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Primordial Sharp Features through the Nonlinear Regime of Structure Formation
Sharp primordial features survive nonlinear structure formation as localised bumps or dips in the matter power spectrum, while their oscillatory patterns are erased, leaving an oscillatory imprint in the halo mass function.
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