Learning from Topology: Cosmological Parameter Estimation from the
  Large-scale Structure
Jacky H. T. Yip Adam Rouhiainen Gary Shiu
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.