LiteBIRD science goals and forecasts: improving sensitivity to inflationary gravitational waves with multitracer delensing
Bibliographic record
Abstract
Abstract We estimate the efficiency of mitigating the lensing B-mode polarization, the so-called delensing, for the LiteBIRD experiment with multiple external data sets of lensing-mass tracers. The current best bound on the tensor-to-scalar ratio, r, is limited by lensing rather than Galactic foregrounds. Delensing will be a critical step to improve sensitivity to r as measurements of r become more and more limited by lensing. In this paper, we extend the analysis of the recent LiteBIRD forecast paper to include multiple mass tracers, i.e., the CMB lensing maps from LiteBIRD and CMB-S4-like experiment, cosmic infrared background, and galaxy number density from Euclid- and LSST-like survey. We find that multi-tracer delensing will further improve the constraint on r by about 20%. In LiteBIRD, the residual Galactic foregrounds also significantly contribute to uncertainties of the B-modes, and delensing becomes more important if the residual foregrounds are further reduced by an improved component separation method.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".