Kavli Affiliate: Zeeshan Ahmed
| First 5 Authors: Federico Bianchini, Dominic Beck, W. L. Kimmy Wu, Zeeshan Ahmed, Sebastian Belkner
| Summary:
We compare multiple foreground-cleaning pipelines for estimating the
tensor-to-scalar ratio, $r$, using simulated maps of the planned CMB-S4
experiment within the context of the South Pole Deep Patch. To evaluate
robustness, we analyze bias and uncertainty on $r$ across various foreground
suites using map-based simulations. The foreground-cleaning methods include: a
parametric maximum likelihood approach applied to auto- and cross-power spectra
between frequency maps; a map-based parametric maximum-likelihood method; and a
harmonic-space internal linear combination using frequency maps. We summarize
the conceptual basis of each method to highlight their similarities and
differences. To better probe the impact of foreground residuals, we implement
an iterative internal delensing step, leveraging a map-based pipeline to
generate a lensing $B$-mode template from the Large Aperture Telescope
frequency maps. Our results show that the performance of the three approaches
is comparable for simple and intermediate-complexity foregrounds, with
$sigma(r)$ ranging from 3 to 5 $times 10^{-4}$. However, biases at the
$1-2sigma$ level appear when analyzing more complex forms of foreground
emission. By extending the baseline pipelines to marginalize over foreground
residuals, we demonstrate that contamination can be reduced to within
statistical uncertainties, albeit with a pipeline-dependent impact on
$sigma(r)$, which translates to a detection significance between 2 and
4$sigma$ for an input value of $r = 0.003$. These findings suggest varying
levels of maturity among the tested pipelines, with the auto- and
cross-spectra-based approach demonstrating the best stability and overall
performance. Moreover, given the extremely low noise levels, mutual validation
of independent foreground-cleaning pipelines is essential to ensure the
robustness of any potential detection.
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