Kavli Affiliate: Susan E. Clark
| First 5 Authors: Yiqi Liu, Yiqi Liu, , ,
| Summary:
We investigate how dust foreground complexity can affect measurements of the
tensor-to-scalar ratio, $r$, in the context of the Simons Observatory, using a
cross-spectrum component separation analysis. Employing a suite of simulations
with realistic Galactic dust emission, we find that spatial variation in the
dust frequency spectrum, parametrized by $beta_d$, can bias the estimate for
$r$ when modeled using a low-order moment expansion to capture this spatial
variation. While this approach performs well across a broad range of dust
complexity, the bias increases with more extreme spatial variation in dust
frequency spectrum, reaching as high as $rsim0.03$ for simulations with no
primordial tensors and a spatial dispersion of $sigma(beta_d)simeq0.3$ —
the most extreme case considered, yet still consistent with current
observational constraints. This bias is driven by changes in the
$ell$-dependence of the dust power spectrum as a function of frequency that
can mimic a primordial $B$-mode tensor signal. Although low-order moment
expansions fail to capture the full effect when the spatial variations of
$beta_d$ become large and highly non-Gaussian, our results show that extended
parametric methods can still recover unbiased estimates of $r$ under a wide
range of dust complexities. We further find that the bias in $r$, at the
highest degrees of dust complexity, is largely insensitive to the spatial
structure of the dust amplitude and is instead dominated by spatial
correlations between $beta_d$ and dust amplitude, particularly at higher
orders. If $beta_d$ does spatially vary at the highest levels investigated
here, we would expect to use more flexible foreground models to achieve an
unbiased constraint on $r$ for the noise levels anticipated from the Simons
Observatory.
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