Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure

The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can be applied to extract this topological information, the optimal method for parameter estimation from this tool remains an open question. To address this, we propose a neural network model to map persistence images to cosmological parameters. Through a parameter recovery test, we demonstrate that our model provides accurate and precise estimates, considerably outperforming a Bayesian inference approach.