Extracting the 21-cm power spectrum and the reionization parameters from mock data sets using artificial neural networks

Abstract

ABSTRACT Detection of the H i 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 H i 21-cm power spectrum from synthetic data sets and extract the reionization parameters from the H i 21-cm power spectrum. For the first time, using a suite of simulations, we present an ANN-based framework capable of extracting the H i 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). We have used a combination of two ANNs sequentially. In the first step, ANN1 predicts the 21-cm power spectrum directly from foreground corrupted synthetic data sets. In the second step, ANN2 predicts the reionization parameters from the predicted H i power spectra from ANN1. The two-step ANN framework can be used as an alternative method to extract the 21-cm power spectrum and the reionization parameters directly from foreground dominated data sets. Our ANN-based framework is trained at a redshift of 9.01, and for $\boldsymbol {k}$ modes in the range, $\rm {0.17\lt {\boldsymbol {k}}\lt 0.37~Mpc^{-1}}$. We have tested the network’s performance with mock data sets corrupted with thermal noise corresponding to 1080 h of observations of the SKA-1 LOW and HERA. We have recovered the H i power spectra from foreground dominated synthetic data sets, with an accuracy of $\approx 95{\!-\!}99{{\ \rm per\ cent}}$. We have achieved an accuracy of $\approx ~81{\!-\!}90{{\ \rm per\ cent}}$ and $\approx ~50{\!-\!}60{{\ \rm per\ cent}}$ for the predicted reionization parameters, for test sets corrupted with thermal noise corresponding to the SKA-1 LOW and HERA, respectively.