Trade-offs in field-level simulation-based inference

Characterize the trade-offs of field-level simulation-based inference in astronomy by determining under what conditions using full field-level information with neural network-based density estimators provides genuine constraint improvements while avoiding overfitting to simulation-specific features, and by establishing validation protocols that ensure reliable generalization beyond the training simulations.

Background

Simulation-based inference enables Bayesian analysis when analytical likelihoods are intractable, including field-level approaches that use entire spatial fields rather than summary statistics. While these methods can potentially extract non-Gaussian information, neural networks risk learning artifacts specific to the training simulations, undermining generalization.

The review highlights that, for field-level inference, the balance between potential gains and risks is not yet well understood. This uncertainty necessitates precise characterization of when field-level methods outperform summary-statistic approaches and how to validate them, especially under domain shifts across different simulations and observational systematics.