Kavli Affiliate: Toshiya Namikawa
| First 5 Authors: Emilie Hertig, Kevin Wolz, Toshiya Namikawa, Antón Baleato Lizancos, Susanna Azzoni
| Summary:
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 CMB polarization $B$-modes. Its
nominal design targets a precision $sigma(r=0) leq 0.003$ without delensing.
Achieving this goal and further reducing uncertainties requires the mitigation
of other sources of large-scale $B$-modes 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
on SO-like simulations. Lensing $B$-modes are synthesised using internal CMB
lensing reconstructions as well as Planck-like CIB maps and LSST-like galaxy
density maps. This $B$-mode template is then introduced into SO’s
power-spectrum-based foreground-cleaning algorithm by extending the likelihood
function to include all auto- and cross-spectra between the lensing template
and the SAT $B$-modes. 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 (SEDs). In the absence of primordial $B$-modes, $sigma(r)$
decreases by 37% as a result of delensing. Tensor modes at the level of
$r=0.01$ are successfully detected by our pipeline. Even with more realistic
foreground models including spatial variations in the dust and synchrotron
spectral properties, we obtain unbiased estimates of $r$ by employing the
moment-expansion method. In this case, 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. (Abridged)
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