Nonlinear reconstruction of 21cm global signal from 21cm power spectrum with artificial neural networks

Abstract

In this paper, we propose a novel method to recover the 21cm global signal from the 21cm power spectrum using artificial neural networks (ANNs). The 21cm global signal is crucial for understanding cosmic evolution from the Dark Ages through the Epoch of Reionization (EoR). While interferometers like LOFAR, MWA, HERA, and SKA focus on detecting the 21cm power spectrum, single-dish experiments such as EDGES target the global signal. Our method utilizes ANNs to establish a connection between these two observables, providing a means to cross-validate independent 21cm line observations. This capability is significant as it allows different observational approaches to verify each other's results, ensuring greater reliability in 21cm cosmology. We demonstrate that our ANN-based approach can accurately recover the 21cm global signal across a wide redshift range (z=7.5-35) from simulated data, even when realistic thermal noise levels, such as those expected from SKA-1, are considered. This cross-validation process strengthens the robustness of 21cm signal analysis, offering a more comprehensive understanding of the early universe.

22 pages, 11 figures, accepted in Research in Astronomy \& Astrophysics

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