Cosmology with Persistent Homology: Parameter Inference via Machine Learning
This work addresses cosmological data analysis for researchers, offering an incremental improvement by applying persistent homology to enhance parameter constraints.
The paper tackles cosmological parameter inference by comparing persistent homology (Persistence Images) to traditional methods (Power Spectrum and Bispectrum) in a machine learning pipeline, finding that persistent homology yields better predictions for parameters like Ω_m, σ_8, n_s, and f_NL^loc, with particular strength in constraining primordial non-Gaussianity.
Building upon [2308.02636], 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 $\{Ω_{\rm m}, σ_8, n_{\rm s}, f_{\rm NL}^{\rm loc}\}$. PIs perform particularly well for $f_{\rm NL}^{\rm loc}$, 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 $f_{\rm NL}^{\rm loc}$ and for $Ω_{\rm m}$. This reveals that clusters and voids (0-cycles and 2-cycles) are most informative for $Ω_{\rm m}$, while $f_{\rm NL}^{\rm loc}$ is additionally informed by filaments (1-cycles).