Planck 2018 results
Bibliographic record
Abstract
We present measurements of the cosmic microwave background (CMB) lensing potential using the final $\textit{Planck}$ 2018 temperature and polarization data. We increase the significance of the detection of lensing in the polarization maps from $5\,σ$ to $9\,σ$. Combined with temperature, lensing is detected at $40\,σ$. We present an extensive set of tests of the robustness of the lensing-potential power spectrum, and construct a minimum-variance estimator likelihood over lensing multipoles $8 \le L \le 400$. We find good consistency between lensing constraints and the results from the $\textit{Planck}$ CMB power spectra within the $\rm{ΛCDM}$ model. Combined with baryon density and other weak priors, the lensing analysis alone constrains $σ_8 Ω_{\rm m}^{0.25}=0.589\pm 0.020$ ($1\,σ$ errors). Also combining with baryon acoustic oscillation (BAO) data, we find tight individual parameter constraints, $σ_8=0.811\pm0.019$, $H_0=67.9_{-1.3}^{+1.2}\,\text{km}\,\text{s}^{-1}\,\rm{Mpc}^{-1}$, and $Ω_{\rm m}=0.303^{+0.016}_{-0.018}$. Combining with $\textit{Planck}$ CMB power spectrum data, we measure $σ_8$ to better than $1\,\%$ precision, finding $σ_8=0.811\pm 0.006$. We find consistency with the lensing results from the Dark Energy Survey, and give combined lensing-only parameter constraints that are tighter than joint results using galaxy clustering. Using $\textit{Planck}$ cosmic infrared background (CIB) maps we make a combined estimate of the lensing potential over $60\,\%$ of the sky with considerably more small-scale signal. We demonstrate delensing of the $\textit{Planck}$ power spectra, detecting a maximum removal of $40\,\%$ of the lensing-induced power in all spectra. The improvement in the sharpening of the acoustic peaks by including both CIB and the quadratic lensing reconstruction is detected at high significance (abridged).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.372 | 0.302 |
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".