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Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering by Modi, Chirag; Pandey, Shivam; Ho, Matthew; Hahn, ChangHoon; Blancard, Bruno R'egaldo-Saint; Wandelt, Benjamin is a scholarly article available to read on EtoBox.

What is Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering about?

Simulation-based inference (SBI) is a promising approach to leverage high fidelity cosmological simulations and extract information from the non-Gaussian, non-linear scales that cannot be modeled analytically. However, scaling SBI to the next generation of cosmological surveys faces the computational challenge of requiring a large number of accurate simulations over a wide range of cosmologies, while simultaneously encompassing large cosmological volumes at high resolution. This challenge can potentially be mitigated by balancing the accuracy and computational cost for different components of the the forward model while ensuring robust inference. To guide our steps in this, we perform a sensitivity analysis of SBI for galaxy clustering on various components of the cosmological simulations: gravity model, halo-finder and the galaxy-halo distribution models (halo-occupation distribution, HOD). We infer the $\sigma_8$ and $\Omega_m$ using galaxy power spectrum multipoles and the bispectrum monopole assuming a galaxy number density expected from the luminous red galaxies observed using the Dark Energy Spectroscopy Instrument (DESI). We find that SBI is insensitive to changing gravity m

Author

Modi, Chirag; Pandey, Shivam; Ho, Matthew; Hahn, ChangHoon; Blancard, Bruno R'egaldo-Saint; Wandelt, Benjamin

Published

2023

Language

EN