Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure
Cosmology and Nongalactic Astrophysics
2025-07-08 v2 Machine Learning
Algebraic Topology
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.
Cite
@article{arxiv.2308.02636,
title = {Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure},
author = {Jacky H. T. Yip and Adam Rouhiainen and Gary Shiu},
journal= {arXiv preprint arXiv:2308.02636},
year = {2025}
}
Comments
7 pages, 4 figures. Accepted to the Synergy of Scientific and Machine Learning Modeling Workshop (ICML 2023) and for publication in Machine Learning: Science and Technology