Feb. 13, 2024, 5:45 a.m. | Jorge Enrique Garc\'ia-Farieta H\'ector J Hort\'ua Francisco-Shu Kitaura

cs.LG updates on arXiv.org arxiv.org

The new generation of galaxy surveys will provide unprecedented data allowing us to test gravity at cosmological scales. A robust cosmological analysis of the large-scale structure demands exploiting the nonlinear information encoded in the cosmic web. Machine Learning techniques provide such tools, however, do not provide a priori assessment of uncertainties. This study aims at extracting cosmological parameters from modified gravity (MG) simulations through deep neural networks endowed with uncertainty estimations. We implement Bayesian neural networks (BNNs) with an enriched …

analysis assessment astro-ph.co astro-ph.im bayesian bayesian deep learning cs.lg data deep learning galaxy gravity information machine machine learning machine learning techniques robust scale stat.ml surveys test tools web will

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