Astrophysical foreground substraction is crucial to retrieve the cosmic microwave background (CMB) polarization out of the observed data. Recent efforts by Leloup et al. and Morshed et al. have been carried out toward the development of a minimally informed component separation method to handle a priori unknown foreground spectral energy distributions (SEDs), while being able to estimate cosmological, foreground, and potentially instrumental parameters, jointly. In this paper, we develop a semianalytical performance forecasting framework for the minimally informed method and we validate it by comparing its results against direct sampling of the harmonic-based likelihood and the pixel domain implementation micmac. We then use the forecasting tool to demonstrate the robustness of the bias correction procedure introduced in the minimally informed approach. We find that a data-driven approach based on the currently available observational data is enough to efficiently regularize the bias of the method.

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• Cosmic microwave background
• Cosmological parameters
• Inflation