Galactic foreground residue biases in cosmic-microwave-background lensing-convergence reconstruction and delensing of B-mode maps

National Centre for Nuclear Research, Pasteura 7, 02-093 Warsaw, Poland

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Received: 4 November 2025
Accepted: 23 February 2026

Abstract

Diffuse contamination from Galactic foreground emission is one of the main concerns for reconstruction of the cosmic-microwave-background (CMB) lensing potential for next-generation CMB polarisation experiments. Using realistic simulations, we investigated the impact of Galactic foreground residuals from multi-frequency foreground-cleaning methods on CMB lensing reconstruction and the de-lensing of B-mode maps. We also assessed how these residuals affect constraints on the tensor-to-scalar ratio for a CMB-S4–like experiment. We paid special attention to studies of the errors coming from the small angular scale non-Gaussianity of the foreground residuals. We show that component separation is essential for the lensing reconstruction that reduces Galactic emission contribution to the lensing reconstruction errors by one order of magnitude. The residual foreground contribution is dominated by terms coming from Gaussian components of the residual maps. Errors coming from non-Gaussian components are around three orders of magnitude smaller than the Gaussian one, even for recent and the most complex models of the Galactic emission considered in this work. Although the bias in the reconstruction errors due to the Gaussian component of the residuals being small, it is comparable to the cosmic-variance limit for the lensing power spectrum. For this reason, we corrected for this bias in the de-lensing of B-mode maps and constraining the tensor-to-scalar ratio. We also show that for the delensed B-mode maps with a simple quadratic estimator, that is, residuals of the Galactic emission after component separation, errors are two orders of magnitude smaller than uncertainties from leftover of the lensing signal. However, for high-sensitivity CMB experiments and more efficient de-lensing algorithms that remove up to 90% of the lensing signal, the foreground residuals will become one of the main sources of errors.