Effect of Template Uncertainties on the WMAP and Planck Measures of the Optical Depth Due to Reionization
Bibliographic record
Abstract
Abstract The reionization optical depth is the most poorly determined of the six ΛCDM parameters fit to CMB anisotropy data. Instrumental noise and systematics have prevented uncertainties from reaching their cosmic variance limit. At present, the data sets providing the most statistical constraining power are the WMAP, Planck LFI, and Planck HFI full-sky polarization maps. As the reprocessed HFI data with reduced systematics are not yet publicly unavailable, we examine determinations of τ using 9 year WMAP and 2015 Planck LFI data, with an emphasis on characterizing potential systematic bias resulting from foreground template and masking choices. We find evidence for a low-level systematic in the LFI polarization data with a roughly common-mode morphology across the LFI frequencies and a spectrum consistent with leakage of intensity signal into the polarization channels. We demonstrate significant bias in the optical depth derived when using the LFI 30 GHz map as a template to clean synchrotron from WMAP data, and recommend against the use of the 2015 LFI 30 GHz polarization data as a foreground template for non-LFI data sets. We find an inconsistency between versions of the 2015 polarized 353 GHz dust templates reconstructed from the Planck likelihood and those from delivered maps, which can affect τ at the 1σ level. The spread in τ values over the ensemble of data combinations we study suggests that systematic uncertainties still contribute significantly to the current uncertainty in τ, but all values are consistent with the range of τ = 0.07 ± 0.02.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".