Abstract
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 zin [0,5] redshift range, a remarkable phenomenon requiring further analysis.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.