128 citations · 1.3k across the 58 of their papers we have counts for
60 papers
Can we learn from matter creation to solve the tension problem?
Emilio Elizalde, Martiros Khurshudyan, Sergei D. Odintsov
The tension problem is studied in the light of a matter creation mechanism (an effective approach to replacing dark energy), the way to define the matter creation rate bein…
Constraints on prospective deviations from the cold dark matter model using a Gaussian Process
Martiros Khurshudyan, Emilio Elizalde
Recently, using Bayesian Machine Learning, a deviation from the cold dark matter model on cosmological scales has been put forward. Such model might replace a proposed non-gravitat…
Swampland criteria and neutrino generation in a non-cold dark matter universe
Martiros Khurshudyan
In this paper, the implications of string Swampland criteria for a dark energy-dominated universe, where we have a deviation from the cold dark matter model, will be discussed. In…
Solving the tension in Gravity through Bayesian Machine Learning
Muhsin Aljaf, Emilio Elizalde, Martiros Khurshudyan +2
Bayesian Machine Learning~(BML) and strong lensing time delay~(SLTD) techniques are used in order to tackle the tension in gravity. The power of BML relies on employ…
Reconstruction of the quintessence dark energy potential from a Gaussian process
Emilio Elizalde, Martiros Khurshudyan, K. Myrzakulov +1
The quintessence dark energy potential is reconstructed in a model-independent way. Reconstruction relies on a Gaussian process and on available expansion-rate data. Specifically,…
An approach to cold dark matter deviation and the tension problem by using machine learning
Emilio Elizalde, Janusz Gluza, Martiros Khurshudyan
In this work, two different models, one with cosmological constant , and baryonic and dark matter (with ), and the other with an dark energy (with $ω_{de} \ne…