November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

Cosmological Parameter Inference Machine Learning Algorithms with Constrained Cosmological Simulations

Not scheduled
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Poster Online Posters


Elena Hernandez Martinez (Ludwig-Maximilian University Munich (LMU, Universitäts Sternwarte))


The ΛCDM model stands as the prevailing framework in cosmology, yet discrepancies between Cosmic Microwave Background (CMB) and late universe probes underscore incomplete understanding of essential cosmological parameters, like Ωm and σ8, which govern matter density and density fluctuations in the Universe. To address the limitations of traditional statistical methods, we have developed a novel set of constrained cosmological simulations known as SLOW. These simulations have demonstrated exceptional precision in replicating observed structures within the Local Universe within a cosmological box of size 500 Mpc/h, rendering them an exemplary testbed for diverse cosmological investigations, including the application of Machine Learning techniques for precise cosmological parameter inference within our Local Universe.

Primary author

Elena Hernandez Martinez (Ludwig-Maximilian University Munich (LMU, Universitäts Sternwarte))

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