Tensions between direct measurements of the lens power spectrum from <i>Planck</i> data
Bibliographic record
Abstract
We apply a recently developed method to directly measure the gravitational lensing power spectrum from CMB power spectra to the Planck satellite data. This method allows us to analyze the tension between the temperature power spectrum and lens reconstruction in a model-independent way. Even when allowing for arbitrary variations in the lensing power spectrum, the tension remains at the $2.4\ensuremath{\sigma}$ level. By separating the lensing and unlensed high redshift information in the CMB power spectra, we also show that under $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ the two are in tension at a similar level, whereas the unlensed information is consistent with lensing reconstruction. These anomalies are driven by the smoother acoustic peaks relative to $\mathrm{\ensuremath{\Lambda}}\mathrm{CDM}$ at $\ensuremath{\ell}\ensuremath{\sim}1250--1500$. Both tensions relax slightly when polarization data are considered. This technique also isolates the one combination of the lensing power spectrum multipoles that the Planck CMB power spectra currently constrain and can be straightforwardly generalized to future data when CMB power spectra constrain multiple aspects of lensing which are themselves correlated with lensing reconstruction.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".