Rubin-LSST

Abstract:

The Simons Observatory (SO), due to start full science operations in early 2025, aims to set tight constraints on inflationary physics by inferring the tensor-to-scalar ratio 𝑟 from measurements of cosmic microwave background (CMB) polarization 𝐵-modes. Its nominal design including three small-aperture telescopes (SATs) targets a precision 𝜎⁡(𝑟=0)≤0.003 without delensing. Achieving this goal and further reducing uncertainties requires a thorough understanding and mitigation of other large-scale 𝐵-mode sources such as Galactic foregrounds and weak gravitational lensing. We present an analysis pipeline aiming to estimate 𝑟 by including delensing within a cross-spectral likelihood, and demonstrate it for the first time on SO-like simulations accounting for various levels of foreground complexity, inhomogeneous noise and partial sky coverage. As introduced in an earlier SO delensing paper, lensing 𝐵-modes are synthesized using internal CMB lensing reconstructions as well as Planck-like cosmic infrared background maps and LSST-like galaxy density maps. We then extend SO’s power-spectrum-based foreground-cleaning algorithm to include all auto- and cross-spectra between the lensing template and the SAT 𝐵-modes in the likelihood function. This allows us to constrain 𝑟 and the parameters of our foreground model simultaneously. Within this framework, we demonstrate the equivalence of map-based and cross-spectral delensing and use it to motivate an optimized pixel-weighting scheme for power spectrum estimation. We start by validating our pipeline in the simplistic case of uniform foreground spectral energy distributions. In the absence of primordial 𝐵-modes, we find that the 1⁢𝜎 statistical uncertainty on 𝑟, 𝜎⁡(𝑟), decreases by 37% as a result of delensing. Tensor modes at the level of 𝑟=0.01 are successfully detected by our pipeline. Even when using more realistic foreground models including spatial variations in the dust and synchrotron spectral properties, we obtain unbiased estimates of 𝑟 both with and without delensing by employing the moment-expansion method. In this case, uncertainties are increased due to the higher number of model parameters, and delensing-related improvements range between 27% and 31%. These results constitute the first realistic assessment of the delensing performance at SO’s nominal sensitivity level.