Orateur
Description
Advancing cosmological parameter inference with reduced uncertainties is a vibrant area of research, especially with the wealth of data from next-generation surveys like Euclid, DESI, and the Vera Rubin Observatory. This talk focuses on Simulation-Based Inference (SBI), utilizing summary statistics such as the Power Spectrum P(k) and Bispectrum B(k). However, these summaries fail to fully harness the non-Gaussian and non-linear features of the cosmological density field. To extract this crucial information, a field-based analysis is preferable, although its success hinges on model architecture, hyperparameter tuning, and the ability to fit high-resolution density fields into GPUs. We introduce a hierarchical approach that combines small-scale information from sub-volumes (patches) with large-scale information from the Power Spectrum. Our method demonstrates enhanced Fisher information about the cosmological parameters compared to using P(k) or B(k) alone.
Day constraints
Preferably in the afternoon, March 1st
| Astrophysics Field | Cosmology |
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