Fast Cosmic Microwave Background Analyses via Correlation Functions
Bibliographic record
Abstract
We propose and implement a fast, universally applicable method for extracting the angular power spectrum ℓ from cosmic microwave background temperature maps by first estimating the correlation function ξ(θ). Our procedure recovers the ℓ using N 2 (but potentially N log N ) operations, where N is the number of pixels. This is in contrast with standard maximum likelihood techniques that require N 3 operations. Our method makes no special assumptions about the map, unlike present-day fast techniques that rely on symmetries of the underlying noise matrix, sky coverage, scanning strategy, and geometry. This makes analysis of megapixel maps without symmetries possible for the first time. The key element of our technique is the accurate multipole decomposition of ξ(θ). The ℓ error bars and cross-correlations are found by using a Monte Carlo approach. We applied our technique to a large number of simulated maps with BOOMERanG (Balloon Observations Of Millimetric Radiation and Geophysics) sky coverage in 81,000 pixels. We used a diagonal noise matrix, with approximately the same amplitude as the BOOMERanG experiment. These studies demonstrate that our technique provides an unbiased estimator of the ℓ . Even though our method is approximate, the error bars obtained are nearly optimal, and they converged only after a few tens of Monte Carlo realizations. Our method is directly applicable for the nondiagonal noise matrix. This and other generalizations, such as minimum variance weighting schemes, polarization, and higher order statistics, are also discussed.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".