Extracting the cosmological 21cm signal Using Artificial Neural Networks

One of the key science goals of most of the ongoing or upcoming low-frequency radio experiments is the detection of the redshifted H1 21cm signal to probe the cosmic Dark Ages, Cosmic Dawn, and Epoch of Reionization (EoR). 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 or as a power spectrum using interferometers. 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. We propose to extract the faint signal from the cocktail of influences using artificial neural networks (ANNs). In this talk, I will present an ANN based framework to extract the 21cm signal parameters from mock observations of the 21-cm Global signal and power spectrum experiments.