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Accurate estimation of the Cosmic Microwave Background (CMB) angular powerspectrum is enticing due to the prospect for precision cosmology it presents.Galactic foreground emissions, however, contaminate the CMB signal and need tobe subtracted reliably in order to lessen systematic errors on the CMBtemperature estimates. Typically bright foregrounds in a region lead to furtheruncertainty in temperature estimates in the area even after some foregroundremoval technique is performed and hence determining the underlying full-skyangular power spectrum poses a challenge. We explore the feasibility ofutilizing artificial neural networks to predict the angular power spectrum ofthe full sky CMB temperature maps from the observed angular power spectrum ofthe partial sky in which CMB temperatures in some bright foreground regions aremasked. We present our analysis at large angular scales with two differentmasks. We produce unbiased predictions of the full-sky angular power spectrumand recover the underlying theoretical power spectrum using neural networks.Our predictions are also uncorrelated to a large extent. We further show thatthe multipole-space covariances of the predictions of full-sky spectra made bythe ANNs are much smaller than those of the estimates obtained using thepseudo-$C_\ell$ method.

An Unbiased Estimator of the Full-sky CMB Angular Power Spectrum at Large Scales using Neural Networks