An open-source toolkit that simulates late-universe cosmological observations and benchmarks MCMC, genetic algorithms, Gaussian processes, Bayesian ridge regression, and neural networks in one pipeline.
A model independent null test on the cosmological constant
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abstract
We use the Om statistic and 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.
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Cosmo-Learn: code for learning cosmology using different methods and mock data
An open-source toolkit that simulates late-universe cosmological observations and benchmarks MCMC, genetic algorithms, Gaussian processes, Bayesian ridge regression, and neural networks in one pipeline.