Poster
in
Workshop: The Synergy of Scientific and Machine Learning Modelling (SynS & ML) Workshop
Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure
Jacky H. T. Yip · Adam Rouhiainen · Gary Shiu
Keywords: [ Persistent Homology ] [ Large-scale Structure ] [ Cosmological Parameter Estimation ] [ convolutional neural networks ]
The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters. While persistent homology can be applied to 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 provides accurate and precise estimates, considerably outperforming a Bayesian inference approach.