  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 model between $N$-body simulations and particle mesh (PM) simulations.
However, changing the halo-finder from friends-of-friends (FoF) 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
analyze 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.
