$B$-sure. Part II. Scattering transforms as robustness test for tensor-to-scalar ratio detection from CMB observations

📅 2026-09-15
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本文提出使用散射变换作为鲁棒性测试方法,以检测宇宙微波背景辐射偏振观测中张量与标量比的前景残差污染问题。
📝 Abstract
Galactic foregrounds represent a major contamination to the measurement of primordial $B$-modes from observations of the Cosmic Microwave Background polarisation. Even after the application of component separation algorithms, foreground residuals may potentially still bias the estimate of the tensor-to-scalar ratio $r$, causing a false detection. In this work, we present the methodology of a robustness test for the validation of an eventual detection of primordial $B$-modes, as obtained by a future, LiteBIRD-like satellite experiment. The goal of the test is to identify the foreground residuals contamination by looking for non-Gaussian properties in the CMB $B$-modes map, recovered through blind component separation algorithms. We adopt scattering transforms (ST) as our summary statistics sensitive to the non-Gaussian features of foreground residuals and to their correlation with foregrounds tracer maps. We characterise and validate the methodology on realistic sky simulations with different levels of foregrounds complexity. The proposed test is able to identify a bias on the tensor-to-scalar ratio of $\gtrsim 10^{-3}$ in $\sim 90\%$ of our simulations, with this bias value being of the same order of the accuracy targeted by LiteBIRD. Additionally, for our particular experimental configuration, the test is passed when the bias is lower than the sensitivity on the $r$ parameter, and no warning is raised. These results provide an important step forward in the development of statistical tools for validating future measurement of cosmological parameters, against foregrounds contamination.
Problem

Research questions and friction points this paper is trying to address.

Galactic Foregrounds
Tensor-to-Scalar Ratio
B-modes
Scattering Transforms
Component Separation
Innovation

Methods, ideas, or system contributions that make the work stand out.

scattering transforms
non-Gaussian features
foreground residuals
tensor-to-scalar ratio
CMB polarization
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