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Neural Network Reconstruction of Late-Time Cosmology and Null Tests
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The prospect of nonparametric reconstructions of cosmological parameters from observational data sets has been a popular topic in the literature for a number of years. This has mainly taken the form of a technique based on Gaussian processes but this approach is exposed to several foundational issues ranging from overfitting to kernel consistency problems. In this work, we explore the possibility of using artificial neural networks (ANN) to reconstruct late-time expansion and large scale structure cosmological parameters. We first show how mock data can be used to design an optimal ANN for both parameters, which we then use with real data to infer their respective redshift profiles. We further consider cosmological null tests with the reconstructed data in order to confirm the validity of the concordance model of cosmology, in which we observe a mild deviation with cosmic growth data.
Forward citations
Cited by 2 Pith papers
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Breaking the Dark Sector Degeneracy with Nonparametric Expansion--Growth Reconstruction
Joint nonparametric expansion–growth reconstruction finds no significant dark-sector interaction or dark-energy dynamics, remaining consistent with ΛCDM over 0≲z≲2.
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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.
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