Suchergebnisse

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∈[0,5] redshift range, a remarkable phenomenon requiring further analysis. ; This work has been partially supported by MINECO (Spain), project FIS2016-76363-P, and by AGAUR (Catalan Government), project 2017-SGR-247. The authors would like to thank the referee for valuable comments which helped to improve the manuscript. We also thank Eoin Colgain for interesting comments.

Two inhomogeneous single-fluid models for the Universe, which are able to naturally solve the H0 tension problem, are discussed. The analysis is based on a Bayesian Machine Learning approach that uses a generative process. The method here adopted allows for constraint of the free parameters of each model by making use of the model itself only. The observable is taken to be the Hubble parameter, obtained from the generative process. Using the full advantages of our method, the models are constrained for two redshift ranges. Namely, first this is done with mock H(z) data over z∈[0,2.5], thus covering known H(z) observational data, which are most helpful to validate the fit results. Then, aiming to extend to redshift ranges to be covered by the most recent ongoing and future planned missions, the models are constrained for the range z∈[0,5], too. Full validation of the results for this extended redshift range will have to wait for some years, for when higher-redshift H(z) data become available. This makes our models fully falsifiable. In addition, our second model is able to explain the BOSS reported value for H(z) at z=2.34. ; The work was carried out with the financial support of the Ministry of Education and Science of the Republic of Kazakhstan, Grant No. BR05236277 (S. D. Odintsov and R.Myrzakulov), Grant No. AP05135123 (R.Myrzakulov) and Grant No. 0118RK00935 while S. D. Odintsov was visiting ENU. This work has been partially supported by MINECO (Spain), Projects No. FIS2016-76363-P and No. PID2019–104397 GB-I00, and by AGAUR (Catalan Government), Project No. 2017-SGR-247. ; Peer reviewed