Extracting the Global 21-cm signal from Cosmic Dawn and Epoch of Reionization in the presence of Foreground and Ionosphere

Detection of redshifted H I 21 -cm emission is a potential probe for investigating the Universe's first billion years. Ho we ver, gi ven the significantly brighter foreground, detecting 21 -cm is observationally difficult. The Earth's ionosphere considerably distorts the signal at low frequencies by introducing directional -dependent effects. Here, for the first time, we report the use of Artificial Neural Networks (ANNs) to extract the global 21 -cm signal characteristics from the composite all-sk y av eraged signal, including foreground and ionospheric effects such as refraction, absorption, and thermal emission from the ionosphere's F and D -layers. We assume a 'perfect' instrument and neglect instrumental calibration and beam effects. To model the ionospheric effect, we considered the static and time -varying ionospheric conditions for the mid -latitude region, where LOFAR is situated. In this work, we trained the ANN model for various situations using a synthetic set of the global 21 -cm signals created by altering its parameter space based on the ' tanh ' parametrized model and the Accelerated Reionization Era Simulations (ARES) algorithm. The obtained result shows that the ANN model can extract the global signal parameters with an accuracy of >= 96 per cent in the final study when we include foreground and ionospheric effects. On the other hand, a similar ANN model can extract the signal parameters from the final prediction data set with an accuracy ranging from 97 to 98 per cent when considering more realistic sets of the global 21 -cm signals based on physical models.