Swampland Criteria and Neutrino Generation in a Non-Cold Dark Matter Universe

Constraints on Prospective Deviations from the Cold Dark Matter Model Using a Gaussian Process

“…The top panel depicts the reconstruction when the kernel is that of Equation (6). The middle panel corresponds to the reconstruction from the kernel given by Equation (7). And the bottom panel is the results for the kernel given by Equation (8).…”

“…There, the learning method was based on the generated expansion rate, which already indicated that a deviation from the cold dark matter paradigm might solve the H 0 problem. Moreover, a sound possibility that the mentioned deviation could replace a proposed non-gravitational interaction between dark energy and dark matter was also put forward (see [6,7]). In this regard, a mere deviation from the cold dark matter paradigm may be considered more natural, instead of having to rely on a mechanism that generates an interaction between two energy sources, which have different acting scales and roles in our universe.…”

“…Constraints on the parameters for the cosmological model where the deviation from the cold dark matter case is described by Equations ( 19) and (20), respectively. The constraints are obtained for three kernels, Equations ( 6), (7), and (8), respectively. The upper part of the table stands for the case when the H 0 value is predicted from the GP.…”

“…The various mechanisms proposed to explain this unexpected discrepancy need to be understood, yet. There is even a hint that the ΛCDM model could be challenged (see [5][6][7] and references therein for more discussion). However, one should stress that, at this stage of research, it is not clear yet if there is a need for new physics or if everything comes from an overseen mistake on the measurement side.…”

“…Recently, motivated by this possibility, clues for a deviation from the cold dark matter scenario have been gained from a learning procedure, which allowed us to advance towards the solution of the H 0 tension problem [6,7]. The learning method was based on a Bayesian (probabilistic) Machine Learning approach and thereby generated expansion rates.…”