Tensors, BICEP2 results, prior dependence, and dust
Bibliographic record
Abstract
We investigate the prior dependence on the inferred spectrum of primordial tensor perturbations, in light of recent results from BICEP2 and taking into account a possible dust contribution to polarized anisotropies. We highlight an optimized parametrization of the tensor power spectrum, and adoption of a logarithmic prior on its amplitude ${A}_{\mathrm{T}}$, leading to results that transform more evenly under change of pivot scale. In the absence of foregrounds the tension between the results of BICEP2 and Planck drives the tensor spectral index ${n}_{\mathrm{T}}$ to be blue tilted in a joint analysis, which would be in contradiction to the standard inflation prediction (${n}_{\mathrm{T}}<0$). When foregrounds are accounted for, the BICEP2 results no longer require nonstandard inflationary parameter regions. We present limits on primordial ${A}_{\mathrm{T}}$ and ${n}_{\mathrm{T}}$, adopting foreground scenarios put forward by Mortonson and Seljak and motivated by Planck 353 GHz observations, and assess what dust contribution leaves a detectable cosmological signal. We find that if there is sufficient dust for the signal to be compatible with standard inflation, then the primordial signal is too weak to be robustly detected by BICEP2 if $\text{Planck}+\mathrm{WMAP}$ upper limits from temperature and $E$-mode polarization are correct.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".