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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 keyscience goals of most of the ongoing or upcoming experiments, for example,EDGES, SARAS, and the SKA. This signal can be detected by averaging over theentire sky, using a single radio telescope, in the form of a Global signal as afunction of only redshifted HI 21cm frequencies. One of the major challengesfaced while detecting this signal is the dominating, bright foreground. Thesuccess of such detection lies in the accuracy of the foreground removal. Thepresence of instrumental gain fluctuations, chromatic primary beam, radiofrequency interference (RFI) and the Earth's ionosphere corrupts anyobservation of radio signals from the Earth. Here, we propose the use ofArtificial Neural Networks (ANN) to extract the faint redshifted 21cm Globalsignal buried in a sea of bright Galactic foregrounds and contaminated bydifferent instrumental models. The most striking advantage of using ANN is thefact that, when the corrupted signal is fed into a trained network, we cansimultaneously extract the signal as well as foreground parameters veryaccurately. Our results show that ANN can detect the Global signal with$\gtrsim 92 \%$ accuracy even in cases of mock observations where theinstrument has some residual time-varying gain across the spectrum.

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