https://doi.org/10.1140/epjc/s10052-025-14256-0
Regular Article - Theoretical Physics
In search of global 21-cm signal using artificial neural network in light of ARCADE 2
1
Department of Physics, National Institute of Technology Meghalaya, Shillong, Meghalaya, India
2
Jodrell Bank Centre for Astrophysics, Department of Physics and Astronomy, University of Manchester, Oxford Road, M13 9PL, Manchester, UK
3
School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
4
University of Chinese Academy of Sciences, Beijing, China
5
RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, 650 0047, Kobe, Hyogo, Japan
Received:
19
March
2024
Accepted:
3
May
2025
Published online:
14
May
2025
Understanding the astrophysical nature of the first stars remains an unsolved problem in cosmology. The redshifted global 21-cm signal 
acts as a treasure trove to probe the cosmic dawn era – when the intergalactic medium was mostly neutral. Many experiments, like SARAS 3, EDGES, and DARE, have been proposed to probe the cosmic dawn era. However, extracting the faint cosmological signal buried inside a brighter foreground, 
, remains challenging. Additionally, an accurate modelling of foreground and 
signal remains the heart of any extraction technique. In this work, we constructed the foreground signal 
from the global sky model and star formation history using Press–Schechter formalism to determine the 
signal with excess radio background following ARCADE 2 detection. Further, we incorporated static ionospheric distortion into the total signal and calculated the signal measured by an ideal antenna. We then trained an artificial neural network (ANN) for the extraction of a signal parameters signal measured by antenna with an R-square score 
. Lastly, we used a Bayesian technique to extract signal and compared the finding with ANN’s extraction.
© The Author(s) 2025
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Funded by SCOAP3.
