Field-level simulation-based inference with galaxy catalogs: the impact
  of systematic effects
Natalí S. M. de Santi Francisco Villaescusa-Navarro L. Raul Abramo Helen Shao Lucia A. Perez Tiago Castro Yueying Ni Christopher C. Lovell Elena Hernandez-Martinez Federico Marinacci David N. Spergel Klaus Dolag Lars Hernquist Mark Vogelsberger
It has been recently shown that a powerful way to constrain cosmological
parameters from galaxy redshift surveys is to train graph neural networks to
perform field-level likelihood-free inference without imposing cuts on scale.
In particular, de Santi et al. (2023) developed models that could accurately
infer the value of $\Omega_{\rm m}$ from catalogs that only contain the
positions and radial velocities of galaxies that are robust to uncertainties in
astrophysics and subgrid models. However, observations are affected by many
effects, including 1) masking, 2) uncertainties in peculiar velocities and
radial distances, and 3) different galaxy selections. Moreover, observations
only allow us to measure redshift, intertwining galaxies' radial positions and
velocities. In this paper we train and test our models on galaxy catalogs,
created from thousands of state-of-the-art hydrodynamic simulations run with
different codes from the CAMELS project, that incorporate these observational
effects. We find that, although the presence of these effects degrades the
precision and accuracy of the models, and increases the fraction of catalogs
where the model breaks down, the fraction of galaxy catalogs where the model
performs well is over 90 %, demonstrating the potential of these models to
constrain cosmological parameters even when applied to real data.