A framework to mitigate patchy reionization contamination on the primordial gravitational wave signal
Bibliographic record
Abstract
ABSTRACT One of the major goals of future cosmic microwave background (CMB) B-mode polarization experiments is the detection of primordial gravitational waves through an unbiased measurement of the tensor-to-scalar ratio r. Robust detection of this signal will require mitigating all possible contamination to the B-mode polarization from astrophysical origins. One such extragalactic contamination arises from the patchiness in the electron density during the reionization epoch. Along with the signature on CMB polarization, the patchy reionization can source secondary anisotropies on the CMB temperature through the kinetic Sunyaev–Zeldovich (kSZ) effect. In order to study the impact of this foreground for the upcoming CMB missions, we present a self-consistent framework to compute the CMB anisotropies based on a physically motivated model of reionization. We show that the value of r can bias towards a higher value if the secondary contribution from reionization is neglected. However, combining small-scale kSZ signal, large-scale E-mode polarization, and B-mode polarization measurements, we can put constraints on the patchiness in electron density during reionization and can mitigate its impact on the value of r. CMB missions such as CMB-S4 and PICO may experience a bias of >0.17σ which can go as high as ∼0.73σ for extreme reionization models allowed by the Planck and SPT CMB measurements. As future experiments target to measure r at 5σ, this is likely to affect the measurement significance and hence possibly affect the claim of detection of r, if not mitigated properly by using joint estimations of different reionization observables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".