Speaker
Description
Simulation-Based Inference (SBI) overcomes problems of likelihood-based estimators allowing for the extraction of information at full-field level. I present a proof-of-concept SBI pipeline to marginally constrain the cosmological parameters $\{\Omega_m, \sigma_8\}$ from large-scale structure observables. Our approach combines fast dark matter simulations with neural emulators that generate galaxy and HI maps. We perform inference both on the power spectrum as a summary statistics and directly on field-level representations of the data, using either one single probe or multiple fields at the same time, while marginalising over baryonic nuisance parameters. We assess systematically the impact of data compression and multi-tracers information on cosmological constraints.
We find that multi-probe analyses, combining galaxy and HI fields, improve constraints with respect to single-probe cases. Moving from summary statistics to field-level inference leads to a significant gain in constraining power, with the full 3D approach providing the most accurate and well-calibrated posteriors. When marginalizing over astrophysical effects, field-level methods retain substantially more cosmological information than power-spectrum-based analyses. These results highlight the potential of combining multi-probes observations and full field information into SBI to fully exploit the information content of forthcoming surveys, and provide a baseline for more realistic observational data-based analyses.
Author
Co-authors
External references
- 26060030
- bbef3b88-8569-4c07-88f8-aa5a4558f22c