Learning from Topology: Cosmological Parameter Estimation from the Large-Scale 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 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 makes accurate and precise estimates, considerably outperforming Bayesian inference approaches.
Cite
Text
Yip et al. "Learning from Topology: Cosmological Parameter Estimation from the Large-Scale Structure." ICML 2023 Workshops: SynS_and_ML, 2023.Markdown
[Yip et al. "Learning from Topology: Cosmological Parameter Estimation from the Large-Scale Structure." ICML 2023 Workshops: SynS_and_ML, 2023.](https://mlanthology.org/icmlw/2023/yip2023icmlw-learning/)BibTeX
@inproceedings{yip2023icmlw-learning,
title = {{Learning from Topology: Cosmological Parameter Estimation from the Large-Scale Structure}},
author = {Yip, Jacky H. T. and Rouhiainen, Adam and Shiu, Gary},
booktitle = {ICML 2023 Workshops: SynS_and_ML},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/yip2023icmlw-learning/}
}