LiteBIRD science goals and forecasts: improved full-sky reconstruction of the gravitational lensing potential through the combination of Planck and LiteBIRD data
Bibliographic record
Abstract
Abstract Cosmic microwave background (CMB) photons are deflected by large-scale structure through gravitational lensing. This secondary effect introduces higher-order correlations in CMB anisotropies, which are used to reconstruct lensing deflections. This allows mapping of the integrated matter distribution along the line of sight, probing the growth of structure, and recovering an undistorted view of the last-scattering surface. Gravitational lensing has been measured by previous CMB experiments, with Planck 's 42 σ detection being the current best full-sky lensing map. We present an enhanced LiteBIRD lensing map by extending the CMB multipole range and including the minimum-variance estimation, leading to a 49 to 58 σ detection over 80 % of the sky, depending on the final complexity of polarized Galactic emission. The combination of Planck and LiteBIRD will be the best full-sky lensing map in the 2030s, providing a 72 to 78 σ detection over 80 % of the sky, almost doubling Planck 's sensitivity. Finally, we explore different applications of the lensing map, including cosmological parameter estimation using a lensing-only likelihood and internal delensing, showing that the combination of both experiments leads to improved constraints. The combination of Planck + LiteBIRD will improve the S 8 constraint by a factor of 2 compared to Planck , and Planck + LiteBIRD internal delensing will improve LiteBIRD 's tensor-to-scalar ratio constraint by 6 %. We have tested the robustness of our results against foreground models of different complexity, showing that improvements remain even for the most complex foregrounds.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".