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

TL;DR

This paper introduces a neural network approach to estimate cosmological parameters from the universe's large-scale structure topology, outperforming traditional Bayesian methods.

Contribution

It presents a novel neural network model that maps persistence images to cosmological parameters, improving estimation accuracy and precision.

Findings

Neural network outperforms Bayesian inference in parameter recovery

Model provides accurate and precise cosmological parameter estimates

Topology-based approach enhances understanding of universe's structure

Abstract

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.