21cm Global Signal Extraction: Extracting the 21cm Global Signal using Artificial Neural Networks

Abstract

The study of the cosmic Dark Ages, Cosmic Dawn, and Epoch of Reionization (EoR) using the all-sky averaged redshifted HI 21cm signal, are some of the key science goals of most of the ongoing or upcoming experiments, for example, EDGES, SARAS, and the SKA. This signal can be detected by averaging over the entire sky, using a single radio telescope, in the form of a Global signal as a function of only redshifted HI 21cm frequencies. One of the major challenges faced while detecting this signal is the dominating, bright foreground. The success of such detection lies in the accuracy of the foreground removal. The presence of instrumental gain fluctuations, chromatic primary beam, radio frequency interference (RFI) and the Earth's ionosphere corrupts any observation of radio signals from the Earth. Here, we propose the use of Artificial Neural Networks (ANN) to extract the faint redshifted 21cm Global signal buried in a sea of bright Galactic foregrounds and contaminated by different instrumental models. The most striking advantage of using ANN is the fact that, when the corrupted signal is fed into a trained network, we can simultaneously extract the signal as well as foreground parameters very accurately. Our results show that ANN can detect the Global signal with $\gtrsim 92\%$ accuracy even in cases of mock observations where the instrument has some residual time-varying gain across the spectrum.

14 pages, 18 figures. Published in MNRAS

References in corpus (7)

• A luminous quasar at a redshift of z = 7.085
• Cosmology at Low Frequencies: The 21 cm Transition and the High-Redshift Universe
• Observational constraints on Cosmic Reionization
• Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks
• A Space-Based Observational Strategy for Characterizing the First Stars and Galaxies Using the Redshifted 21-cm Global Spectrum
• Reconstructing the nature of the first cosmic sources from the anisotropic 21-cm signal
• Selection between foreground models for global 21-cm experiments

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• Comparing sampling techniques to chart parameter space of 21 cm Global signal with Artificial Neural Networks
• Impact of Calibration and Position Errors on Astrophysical Parameters of the HI 21cm Signal
• From ANN to BNN: Inferring Reionization Parameters using Uncertainty-aware Emulators of 21-cm Summaries
• Accelerating reionization constraints: An ANN-emulator framework for the SCRIPT Semi-numerical Model
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