Contributor info: Mr Benedikt Schosser

Benedikt Schosser, Caroline Heneka, Tilman Plehn

SciPost Phys. Core 8, 037 (2025) · published 10 April 2025 | Toggle abstract · pdf

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 efficient and extremely fast determination of the posteriors of astrophysical and cosmological parameters, jointly with well-calibrated and on average unbiased summaries. The sensitivity to non-Gaussian information makes our method a promising alternative to the established power spectra.

Tanmoy Modak, Lennart Röver, Björn Malte Schäfer, Benedikt Schosser, Tilman Plehn

SciPost Phys. 15, 047 (2023) · published 3 August 2023 | Toggle abstract · pdf

Starobinsky inflation is an attractive, fundamental model to explain the Planck measurements, and its higher-order extension may allow us to probe quantum gravity effects. We show that future CMB data combined with the 21cm intensity map from SKA will meaningfully probe such an extended Starobinsky model. A combined analysis will provide a precise measurement and intriguing insight into inflationary dynamics, even accounting for correlations with astrophysical parameters.