Sensitivity analysis of simulation-based inference for galaxy clustering

Abstract

ABSTRACT 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 modelled 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 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 $\Omega _\mathrm{ m}$ 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. We find that SBI is insensitive to changing gravity model between N-body simulations and particle mesh simulations. However, changing the halo finder from friends of friends to Rockstar can lead to biased estimate of $\sigma _8$ based on the bispectrum. For galaxy models, training SBI on more complex HOD leads to consistent inference for less complex HOD models, but SBI trained on simpler HOD models fails when applied to analyse data from a more complex HOD model. Based on our results, we discuss the outlook on cosmological simulations with a focus on applying SBI approaches to future galaxy surveys.

Topics & Concepts