https://doi.org/10.1140/epjc/s10052-021-09130-8
Regular Article - Theoretical Physics
Interplay between Swampland and Bayesian Machine Learning in constraining cosmological models
1
Consejo Superior de Investigaciones Científicas, ICE/CSIC-IEEC, Campus UAB, Carrer de Can Magrans s/n, 08193, Bellaterra, Barcelona, Spain
2
International Laboratory for Theoretical Cosmology, Tomsk State University of Control Systems and Radioelectronics (TUSUR), 634050, Tomsk, Russia
3
Institute of Physics, University of Silesia, Katowice, Poland
Received:
2
September
2020
Accepted:
10
April
2021
Published online:
20
April
2021
Constraints on a dark energy dominated Universe are obtained from an interplay Bayesian (Probabilistic) Machine Learning and string Swampland criteria. Unlike in previous studies, here, the field traverse itself has been used to constraint the theory and reveal its connection to the Swampland approach. The field traverse based Bayesian (Probabilistic) Learning approach is applied to two toy models. A parametrization of the Hubble constant is used for the first model, while a parametrization of the deceleration parameter is considered for the second one. The results obtained here allow to estimate how the high-redshift behavior of the Universe will affect the low-redshift one. Moreover, the adopted approach may highlight, in the future, the borders of the Swampland for the low-redshift Universe and help to develop new string-theory motivated dark energy models. The most important message from our study is a hint that the string Swampland criteria might be in tension with recent observations indicating that phantom dark energy cannot be in the Swampland. Finally, another interesting result obtained in our study is a spontaneous sign switch in the dark energy equation of state parameter when the field traverses are in the ![$$z\in [0,5]$$](/articles/epjc/abs/2021/04/10052_2021_Article_9130/10052_2021_Article_9130_tex_eq1.png)
redshift range, a remarkable phenomenon requiring further analysis.
The original online version of this article was revised: The first sentence of the conclusion section was incomplete. The correct wording reads: “We say nothing new when stating that Machine Learning,.....”.
An erratum to this article is available online at https://doi.org/10.1140/epjc/s10052-021-09191-9.
© The Author(s) 2021
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