Requirements on bandpass resolution and measurement precision for LiteBIRD
Bibliographic record
Abstract
Abstract Systematic effects can hinder the sought-after detection of primordial gravitational waves, impacting the reconstruction of the B -mode polarization signal which they generate in the cosmic microwave background (CMB). In this work, we study the impact of an imperfect knowledge of the instrument bandpasses on the estimate of the tensor-to-scalar ratio r in the context of the next-generation LiteBIRD satellite. We develop a pipeline to integrate over the bandpass transmission in both the time-ordered data (TOD) and the map-making processing steps. We introduce the systematic effect by having a mismatch between the “real”, high resolution bandpass τ , entering the TOD, and the estimated one τ s , used in the map-making. We focus on two aspects: the effect of degrading the τ s resolution, and the addition of a Gaussian error σ to τ s . To reduce the computational load of the analysis, the two effects are explored separately, for three representative LiteBIRD channels (40 GHz, 140 GHz and 402 GHz) and for three bandpass shapes. Computing the amount of bias on r , Δ r , caused by these effects on a single channel, we find that a resolution ≲ 1.5 GHz and σ ≲ 0.0089 do not exceed the LiteBIRD budget allocation per systematic effect, Δ r < 6.5 × 10 -6 . We then check that propagating separately the uncertainties due to a resolution of 1 GHz and a measurement error with σ = 0.0089 in all LiteBIRD frequency channels, for the most pessimistic bandpass shape of the three considered, still produces a Δ r < 6.5 × 10 -6 . This is done both with the simple deprojection approach and with a blind component separation technique, the Needlet Internal Linear Combination (NILC). Due to the effectiveness of NILC in cleaning the systematic residuals, we have tested that the requirement on σ can be relaxed to σ ≲ 0.05.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".