Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure
This work addresses a specific bottleneck in cosmology for researchers, offering a novel method for parameter estimation from topological data.
The authors tackled the problem of estimating cosmological parameters from the topology of the universe's large-scale structure by proposing a neural network model that maps persistence images to parameters, resulting in accurate and precise estimates that outperform conventional Bayesian inference approaches.
The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topological information, the optimal method for parameter estimation from the 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 makes accurate and precise estimates, considerably outperforming conventional Bayesian inference approaches.