Multiwavelength constraints on the inflationary consistency relation
Bibliographic record
Abstract
We present the first attempt to use a combination of CMB, LIGO, and Pulsar Timing Array (PPTA) data to constrain both the tilt and the running of primordial tensor power spectrum through constraints on the gravitational wave energy density generated in the early universe. Combining measurements at different cosmological scales highlights how complementary data can be used to test the predictions of early universe models including the inflationary consistency relation. Current data prefer a slightly positive tilt (${n}_{t}=0.0{6}_{\ensuremath{-}0.89}^{+0.63}$) and a negative running (${n}_{t,\text{run}}<\ensuremath{-}0.22$) for the tensor power spectrum spectrum. Interestingly, the addition of direct gravitational wave detector data alone puts strong bounds on the tensor-to-scalar ratio $r<0.2$ since the large positive tensor tilt preferred by the Planck temperature power spectrum is no longer allowed. Adding the recently released BICEP2/KECK and Planck 353 GHz polarization cross-correlation data gives an even stronger bound $r<0.1$. We comment on possible effects of a large positive tilt on the background expansion and show that depending on the assumptions regarding the UV cutoff (${k}_{\mathrm{UV}}/{k}_{*}=1{0}^{24}$) of the primordial spectrum of gravitational waves, the strongest bounds on ${n}_{t}=0.0{7}_{\ensuremath{-}0.80}^{+0.52}$ are derived from this effect.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".