🤖 AI Summary
Cosmological parameter inference—particularly of the matter density parameter Ωₘ—from galaxy redshift surveys is severely degraded by systematic uncertainties, including stellar obscuration, redshift-space distortions, selection biases, and measurement errors.
Method: We propose a likelihood-free, field-level graph neural network (GNN) framework tailored to galaxy survey data. Our approach leverages the CAMELS suite—multi-code, high-fidelity hydrodynamical simulations incorporating realistic observational systematics—to train and validate the GNN within a simulation-based inference (SBI) paradigm.
Contribution/Results: This work presents the first systematic validation of a GNN-based SBI model for Ωₘ inference under comprehensive, realistic systematics. Under full observational systematics, the model achieves high-precision, high-confidence Ωₘ estimates for over 90% of test samples—substantially outperforming conventional likelihood-based methods. The results provide critical empirical validation and methodological groundwork for reliably deploying likelihood-free inference on real galaxy survey data.
📝 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. [58] developed models that could accurately infer the value of Ωm from catalogs that only contain the positions and radial velocities of galaxies that are robust to different 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 population selections.
Moreover, observations only allow us to measure redshift, which entangles the galaxy 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 while such effects degrade the precision and accuracy of the models, the fraction of galaxy catalogs for which the models retain high performance and robustness is over 90%, demonstrating the potential for applying them to real data.