Speaker
Benedikt Schosser
(Universität Heidelberg, ARI)
Description
Modern machine learning will allow for simulation-based inference from reionization-era 21cm observations at the Square Kilometre Array. Our framework combines a convolutional summary network and a conditional invertible network through a physics-inspired latent representation. It allows for an optimal and extremely fast determination of the posteriors of astrophysical and cosmological parameters. The sensitivity to non-Gaussian information makes our method a promising alternative to the established power spectra.
Primary authors
Benedikt Schosser
(Universität Heidelberg, ARI)
Caroline Heneka
(Universität Heidelberg, ITP)
Tilman Plehn
(Universität Heidelberg, ITP)
