Cosmology with persistent homology: parameter inference via machine learning

Abstract

Building upon previous work [1], we investigate the constraining power of persistent homology on cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pipeline utilizing machine learning. We evaluate the ability of Persistence Images (PIs) to infer parameters, comparing them to the combined Power Spectrum and Bispectrum (PS/BS). We also compare two classes of models: neural-based and tree-based. PIs consistently lead to better predictions compared to the combined PS/BS for parameters that can be constrained, i.e., for {Ω_m, σ ₈, n _s, f _NL ^loc}. PIs perform particularly well for f _NL ^loc, highlighting the potential of persistent homology for constraining primordial non-Gaussianity. Our results indicate that combining PIs with PS/BS provides only marginal gains, indicating that the PS/BS contains little additional or complementary information to the PIs. Finally, we provide a visualization of the most important topological features for f _NL ^loc and for Ω_m. This reveals that clusters and voids (0-cycles and 2-cycles) are most informative for Ω_m, while f _NL ^loc is additionally informed by filaments (1-cycles).