A Generative Reconstruction of Low-$\ell$ CMB B-Mode Signal using Reverse Diffusion in Deep Learning

Abstract

Detecting primordial B-mode polarization of the Cosmic Microwave Background (CMB) provides a direct probe of inflationary gravitational waves. However, the signal is extremely faint and contaminated by gravitational lensing, instrumental noise, and astrophysical foregrounds. Here we present a score-based diffusion approach, formulated using variance-exploding stochastic differential equations (VE-SDEs), to reconstruct the primordial B-mode angular power spectrum from contaminated observations. The method employs a reverse SDE guided by a score model trained exclusively on random realizations of the primordial low $\ell$ B-mode angular power spectrum corresponding to a fixed tensor-to-scalar ratio $r=0.001$. During inference, the reverse SDE iteratively drives the observed angular power spectrum toward the learned primordial manifold, effectively denoising and delensing the input. The model is tested on simulated observational spectra that include gravitational lensing, complex polarized foreground combinations, and instrumental noise characteristics representative of the proposed ECHO mission. The trained score model learns the underlying statistical distribution of the primordial B-mode field for the given $r$, which acts as a physics-guided prior that can generate new, consistent realizations of the signal. This approach provides a robust framework for primordial signal recovery in future CMB polarization missions.