The Explainable Universe working group at CosmicAI is focused on linking physical theories of cosmic structure formation with interpretable machine learning methods to enable robust and explainable inference from complex astronomical data. This effort is organized around two complementary thrusts: (1) the generation of large, systematically varied suites of cosmological simulations to probe the nature of Dark Matter and cosmic structure formation, and (2) the development of explainable AI methods for scientific inference under realistic, imperfect data conditions.

On the astrophysics side, the group is using commonly employed codes, including AREPO, RAMSES, and GIZMO, and to generate controlled cosmological simulations suites. In distinct contrast to previous studies (e.g., like Illustris, IllustrisTNG, Simba, FIREBox, etc.), these simulation suites include thousands of simulations that systematically vary the input physical assumptions (e.g., the properties of Dark Matter) to create maps linking changed physical assumptions to identifiable impacts on galaxy formation and galaxy properties. By producing a broad library of realizations, we can trace how fundamental physics shapes observable structures in the Universe. This work closely intertwined with the broader DREAMS collaboration and will scale toward larger, more diverse parameter spaces in upcoming runs.

On the AI side, the group develops simulation-based inference (SBI) pipelines that pair these simulation suites with machine learning models capable of drawing probabilistic and interpretable conclusions about underlying cosmological parameters. Methods under development include graph neural networks for learning structural relationships between galaxies and their environments, uncertainty-aware inference models to quantify confidence, and explainability techniques such as saliency mapping and feature attribution to identify which physical signatures most strongly constrain model parameters. The emphasis is on explainability by design — ensuring that models provide insight into why they reach specific conclusions.

A central challenge in cosmology is that observational data are incomplete and noisy, with biases that can obscure underlying physics. Our inference framework explicitly addresses this through robustness testing, domain adaptation, and uncertainty calibration. These steps allow us to produce trustworthy scientific inferences even when real data deviate from the idealized training sets. This capability is particularly crucial for leveraging upcoming large-scale survey missions, which will provide unprecedented but complex data.

Looking forward, the Explainable Universe working group will expand both sides of this pipeline: scaling the astrophysical simulation suites to encompass more physical models and initial conditions, and advancing the AI frameworks to support explainable, uncertainty-aware inference at survey scale. This approach aims to turn astronomical observations into transparent, scientifically grounded constraints on the nature of Dark Matter and cosmic structure formation.