128 citations · 1.3k across the 58 of their papers we have counts for
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Assessing the foundation and applicability of some dark energy fluid models in the Dirac-Born-Infeld framework
Muhsin Aljaf, Daniele Gregoris, Martiros Khurshudyan
In this paper, we will deepen the understanding of some fluid models proposed by other authors for the description of dark energy. Specifically, we will show that the so-called (Mo…
Interplay between Swampland and Bayesian Machine Learning in constraining cosmological models
Emilio Elizalde, Martiros Khurshudyan
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 previ…
An analysis of the tension problem in a universe with a viscous dark fluid
Emilio Elizalde, Martiros Khurshudyan, Sergei D. Odintsov +1
In this paper, two inhomogeneous single fluid models for the Universe, which are able to naturally solve the tension problem, are discussed. The analysis is based on a Baye…
Constraints on Cosmic Opacity from Bayesian Machine Learning: The hidden side of the tension problem
Emilio Elizalde, Martiros Khurshudyan
Bayesian (Probabilistic) Machine Learning is used to probe the opacity of the Universe. It relies on a generative process where the model is the key object to generate the data inv…
Constraints on interacting dark energy models through cosmic chronometers and Gaussian process
Muhsin Aljaf, Daniele Gregoris, Martiros Khurshudyan
In this paper, after reconstructing the redshift evolution of the Hubble function by adopting Gaussian process techniques, we estimate the best-fit parameters for some flat Friedma…