Genetic algorithms and the analysis of SnIa data

TL;DR

This paper employs genetic algorithms to reconstruct the universe's expansion history from supernova data and tests the consistency of the Lambda Cold Dark Matter model without assuming specific cosmological parameters.

Contribution

It introduces a model-independent method using genetic algorithms to analyze supernova data and perform a null test on the flat Lambda CDM model.

Findings

Lambda CDM is consistent at 2 sigma with the data.

Some deviations from Lambda CDM are possible at low redshifts.

Abstract

The Genetic Algorithm is a heuristic that can be used to produce model independent solutions to an optimization problem, thus making it ideal for use in cosmology and more specifically in the analysis of type Ia supernovae data. In this work we use the Genetic Algorithms (GA) in order to derive a null test on the spatially flat cosmological constant model $\Lambda$CDM. This is done in two steps: first, we apply the GA to the Constitution SNIa data in order to acquire a model independent reconstruction of the expansion history of the Universe $H(z)$ and second, we use the reconstructed $H(z)$ in conjunction with the Om statistic, which is constant only for the $\Lambda$CDM model, to derive our constraints. We find that while $\Lambda$CDM is consistent with the data at the $2\sigma$ level, some deviations from $\Lambda$CDM model at low redshifts can be accommodated.