The Simons Observatory: Combining delensing and foreground cleaning for improved constraints on inflation

TL;DR

The paper presents an analysis pipeline for the Simons Observatory that simultaneously performs delensing and foreground cleaning, improving constraints on primordial gravitational waves by reducing uncertainty in the tensor-to-scalar ratio.

Contribution

It introduces a novel combined delensing and foreground cleaning algorithm that incorporates multifrequency data and lensing templates within a power-spectrum likelihood framework.

Findings

Delensing reduces the uncertainty in r by 27-37%.

The pipeline achieves the target precision for SO's design.

The method effectively handles inhomogeneous noise and non-Gaussian foregrounds.

Abstract

The Simons Observatory (SO), a next-generation ground-based CMB experiment in its final stages of construction, will target primordial $B$-modes with unprecedented sensitivity to set tight bounds on the amplitude of inflationary gravitational waves. Aiming to infer the tensor-to-scalar ratio $r$ with precision $\sigma(r=0) \leq 0.003$, SO will rely on powerful component-separation algorithms to distinguish the faint primordial signal from stronger sources of large-scale $B$-modes such as Galactic foregrounds and weak gravitational lensing. We present an analysis pipeline that performs delensing and foreground cleaning simultaneously by including multifrequency CMB data and a lensing $B$-mode template in a power-spectrum-based likelihood. Here, we demonstrate this algorithm on masked SO-like simulations containing inhomogeneous noise and non-Gaussian foregrounds. The lensing convergence is reconstructed from high-resolution simulations of the CMB and external mass tracers. Using optimized pixel weights for power spectrum estimation, the target precision for SO's nominal design is achieved and delensing reduces $\sigma(r)$ by 27-37%, depending on foreground complexity.