Extracting the 21-cm Power Spectrum and the reionization parameters from mock datasets using Artificial Neural Networks

Abstract

Detection of the \hi~ 21-cm power spectrum is one of the key science drivers of several ongoing and upcoming low-frequency radio interferometers. However, the major challenge in such observations come from bright foregrounds, whose accurate removal or avoidance is key to the success of these experiments. In this work, we demonstrate the use of artificial neural networks (ANNs) to extract the \hi~ 21-cm power spectrum from synthetic datasets and extract the reionization parameters from the \hi~ 21-cm power spectrum. For the first time, using a suite of simulations, we present an ANN based framework capable of extracting the \hi~ signal power spectrum directly from the total observed sky power spectrum (which contains the 21-cm signal, along with the foregrounds and effects of the instrument). To achieve this, we have used a combination of two separate neural networks sequentially. As the first step, \texttt{ANN1} predicts the 21-cm power spectrum directly from foreground corrupted synthetic datasets. In the second step, \texttt{ANN2} predicts the reionization parameters from the predicted \hi~ power spectra from \texttt{ANN1}. Our ANN-based framework is trained at a redshift of $9.01$, and for \kk-modes in the range, $\rm{0.17<\kk<0.37~Mpc^{-1}}$. We have tested the network's performance with mock datasets that include foregrounds and are corrupted with thermal noise, corresponding to $1080$ hrs of observations of the \textsc{ska-1 low} and \textsc{hera}. Using our ANN framework, we are able to recover the \hi~ power spectra with an accuracy of $\approx 95-99\%$ for the different test sets. For the predicted astrophysical parameters, we have achieved an accuracy of $\approx 81-90\%$ and $\approx 50-60\%$ for the test sets corrupted with thermal noise corresponding to the \textsc{ska-1 low} and \textsc{hera}, respectively.

Submitted to MNRAS, under review

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• Extracting the Global 21-cm signal from Cosmic Dawn and Epoch of Reionization in the presence of Foreground and Ionosphere
• Efficient exploration of reionization parameters for the upcoming 21 cm observations using a photon conserving semi-numerical model SCRIPT
• Comparing sampling techniques to chart parameter space of 21 cm Global signal with Artificial Neural Networks
• Probing the Epoch of Reionization using synergies of line intensity mapping
• Synthetic Observations with the Square Kilometre Array (SKA) -- development towards an end-to-end pipeline
• Impact of Calibration and Position Errors on Astrophysical Parameters of the HI 21cm Signal
• Deep Needlet: A CNN based full sky component separation method in Needlet space
• 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