Extended models of gravity in SNIa cosmological data using genetic algorithms

Abstract

In this talk I explained briefly the advantages of using genetic algorithms on any measured data but specially astronomical ones. This kind of algorithms are not only a better computational paradigm, but they also allow for a more profound data treatment enhancing theoretical developments. As an example, I will use the SNIa cosmological data to fit the extended metric theories of gravity of Carranza et al. (2013, 2014) showing that the best parameters combination deviate from theoretical predicted ones by a minimal amount. This means that these kind of gravitational extensions are statistically robust and show that no dark matter and/or energy is required to explain the observations.

arXiv comment3 figures, 6 pages, Proceedings of the "Encuentros Realtivistas Españoles 2015" Conference

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Cited by in corpus (2)

• The Case Against Dark Matter and Modified Gravity: Flat Rotation Curves Are a Rigorous Requirement in Rotating Self-Gravitating Newtonian Gaseous Disks
• Dynamics of clusters of galaxies with extended $f(\chi )$ gravity