<ul><li>This article investigates the potential of persistent homology for constraining cosmological parameters and primordial non-Gaussianity amplitudes.</li><li>Persistent homology using persistence images (PIs) performs better than the combined Power Spectrum and Bispectrum (PS/BS) for inferring parameters.</li><li>PIs show promise in constraining primordial non-Gaussianity, particularly for the parameter fNL^loc.</li><li>The combination of PIs with PS/BS provides only marginal gains, indicating little extra information in PS/BS compared to PIs.</li></ul>

Cosmology with Persistent Homology: Parameter Inference via Machine Learning

Discover more