Interplay between Swampland and Bayesian Machine Learning in constraining cosmological models
Emilio Elizalde, Martiros Khurshudyan

TL;DR
This paper integrates Bayesian Machine Learning with string Swampland criteria to constrain cosmological models, providing a data-independent approach that explores the high-redshift universe's impact on low-redshift behavior.
Contribution
It introduces a novel, data-independent Bayesian framework using Swampland criteria to constrain cosmological models across redshifts, independent of dark energy assumptions.
Findings
High-redshift behavior influences low-redshift constraints.
Spontaneous sign switch in dark energy equation of state observed.
Constraints may challenge the compatibility of phantom dark energy with the Swampland.
Abstract
Constraints on a dark energy dominated Universe are obtained from an interplay between Bayesian Machine Learning and string Swampland criteria. The approach here differs from previous studies, since in the generative process Swampland criteria are used and, only later, the results of the fit are validated, by using observational data-sets. A generative process based Bayesian Learning approach is applied to two models and the results are validated by means of available data. For the first model, a parametrization of the Hubble constant is considered and, for the second, a parametrization of the deceleration parameter. This study is motivated by a recent work, where constraints on string Swampland criteria have been obtained from a Gaussian Process and data. However, the results obtained here are fully independent of the observational data and allow to estimate how the…
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