Extended models of gravity in SNIa cosmological data using genetic algorithms

TL;DR

This paper demonstrates that genetic algorithms can effectively analyze supernova data to test extended gravity models, showing they fit observations well without needing dark matter or energy.

Contribution

It introduces the use of genetic algorithms for analyzing SNIa data in the context of extended gravity theories, highlighting their computational and theoretical advantages.

Findings

Genetic algorithms provide a robust method for fitting cosmological data.

Extended gravity models can explain observations without dark matter/energy.

Best-fit parameters deviate minimally from theoretical predictions.

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.