Speaker
Description
In recent years, generative diffusion models have made rapid progress in learning complex data distributions. In cosmology, they have shown promising results in emulating simulations across diverse cosmological settings, mitigating their computational cost while capturing not only two-point statistics but also higher-order correlations. In this work, we construct an optimised generative diffusion model to emulate the cosmic web (CW), comprising voids, walls, filaments and nodes, which encodes key information about cosmological parameters and the evolution of the large-scale structure in the Universe. In particular, we investigate how diffusion models capture the statistical properties of the CW via self-attention maps by evaluating how well they reproduce distinct CW environments. Based on several statistical estimators, our analysis provides a quantitative assessment of the performance of diffusion models. We further explore how latent generative models learn high-resolution 3D simulation by evaluating the robustness of this approach in preserving CW statistics.