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Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks
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Constraining cosmological parameters from N-body simulations with Bayesian Neural Networks
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In this paper, we use The Quijote simulations in order to extract the cosmological parameters through Bayesian Neural Networks. This kind of model has a remarkable ability to estimate the associated uncertainty, which is one of the ultimate goals in the precision cosmology era. We demonstrate the advantages of BNNs for extracting more complex output distributions and non-Gaussianities information from the simulations.
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Cited by 1 Pith paper
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Learning Cosmology from Nearest Neighbour Statistics
Nearest-neighbour distance maps, combined with kNN-CDFs in a hybrid neural network, constrain Ωm and σ8 from Quijote halos with R2=0.80 and 0.93, matching or beating point-cloud methods at a fraction of the compute.
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