Robust CMB B-mode analysis with Needlet-ILC and simulation-based inference

Abstract

Polarized Galactic emission is the foremost challenge for searches for a background of primordial gravitational waves imprinted in the polarization of the CMB. We argue that current methods struggle to address this challenge, either by being overly susceptible to model misspecification, or by failing to properly propagate the uncertainty due to residual Galactic emission after foreground cleaning. To address these issues, we explore a novel analysis framework for parameter inference with large-scale CMB polarization data. Our method combines simulation-based inference with the needlet internal linear combination (NILC) algorithm to compress the data into a summary statistic that is robust to model misspecification and small enough for neural posterior estimation with normalizing flows. We show that the semi-blind nature of the NILC-based compression significantly increases robustness to mismodeling of the anisotropic and non-Gaussian properties of the foreground fields. Using an idealized ground-based setup inspired by the Simons Observatory Small Aperture Telescopes, we demonstrate improved statistical constraining power for the tensor-to-scalar ratio r and improved robustness to complex foregrounds compared to other techniques in the literature. Trained on a semi-analytical foreground model, the method yields unbiased results across a range of PySM simulations, including the high-complexity d12 model, for which we obtain r=(1.09±0.27)⋅1e−2 for input r=0.01 and sky fraction fsky=0.21. Our results highlight the importance of designing data compression schemes for SBI that prioritize robustness to model misspecification over statistical optimality, and demonstrate the feasibility and advantages of a complete maps-to-parameters simulation-based analysis of large-scale CMB polarization for current ground-based observatories.