Topology and Power-Spectrum Audits of Generative Cosmic-Web Simulators
DOI:
https://doi.org/10.66857/jirt.v3i2.59Keywords:
Cosmic Web, Generative Models, Topological Data Analysis, Power Spectrum, CosmologyAbstract
The acceleration of cosmological research relies heavily on accurate simulations of the cosmic web, a vast network of dark matter and baryonic gas spanning the observable universe. While traditional N-body simulations offer high fidelity, their computational expense has driven the development of generative deep learning models. These generative cosmic-web simulators promise orders-of-magnitude speedups but require rigorous validation to ensure they reproduce physically meaningful structures. This paper presents a comprehensive framework for auditing generative cosmic-web simulators using two distinct but complementary approaches: power-spectrum analysis and topological data analysis. The power-spectrum audit evaluates the two-point correlation statistics, ensuring that the distribution of mass at various spatial scales matches theoretical and physical benchmarks. Conversely, the topological audit employs persistent homology and Minkowski functionals to assess higher-order structural connectivity, such as the formation of filaments, nodes, and cosmic voids. By applying these dual audits to contemporary generative models, this study identifies critical divergences in non-linear regimes and topological anomalies often missed by standard summary statistics. The findings underscore the necessity of incorporating both spectral and topological metrics into the loss functions or evaluation pipelines of generative models. This dual-audit methodology provides a robust standard for the cosmological community to validate synthetic universe generations, ultimately paving the way for more reliable machine learning applications in precision cosmology.
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