Parameters Estimation from the 21 cm signal using Variational Inference
This work addresses data processing challenges in cosmology for upcoming 21 cm experiments, offering an automated method for parameter estimation, but it is incremental as it applies an existing technique to a new domain.
The paper tackles the challenge of processing large 21 cm signal data from experiments like HERA and SKA by using Variational Inference with Bayesian Neural Networks to estimate cosmological and astrophysical parameters and their uncertainties, providing credible estimations and assessing correlations as an alternative to MCMC methods.
Upcoming experiments such as Hydrogen Epoch of Reionization Array (HERA) and Square Kilometre Array (SKA) are intended to measure the 21cm signal over a wide range of redshifts, representing an incredible opportunity in advancing our understanding about the nature of cosmic Reionization. At the same time these kind of experiments will present new challenges in processing the extensive amount of data generated, calling for the development of automated methods capable of precisely estimating physical parameters and their uncertainties. In this paper we employ Variational Inference, and in particular Bayesian Neural Networks, as an alternative to MCMC in 21 cm observations to report credible estimations for cosmological and astrophysical parameters and assess the correlations among them.