Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects

TL;DR

This paper evaluates the robustness of graph neural network-based field-level inference models for cosmological parameters when applied to galaxy catalogs affected by realistic observational systematic effects.

Contribution

It extends previous models to include observational effects like masking and velocity uncertainties, demonstrating their resilience on simulated galaxy catalogs.

Findings

Models maintain over 90% accuracy despite observational effects.

Systematic effects degrade but do not eliminate model performance.

Models are promising for real data application in cosmology.

Abstract

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.