Non-Gaussian errors of baryonic acoustic oscillations
Bibliographic record
Abstract
We revisit the uncertainty in baryon acoustic oscillation (BAO) forecasts and data analyses. In particular, we study how much the uncertainties on both the measured mean dilation scale and the associated error bar are affected by the non-Gaussianity of the non-linear density field. We examine two possible impacts of non-Gaussian analysis: (1) we derive the distance estimators from Gaussian theory, but use 1000 N-Body simulations to measure the actual errors, and compare this to the Gaussian prediction, and (2) we compute new optimal estimators, which requires the inverse of the non-Gaussian covariance matrix of the matter power spectrum. Obtaining an accurate and precise inversion is challenging, and we opted for a noise reduction technique applied on the covariance matrices. By measuring the bootstrap error on the inverted matrix, this work quantifies for the first time the significance of the non-Gaussian error corrections on the BAO dilation scale. We find that the variance (error squared) on distance measurements can deviate by up to 12% between both estimators, an effect that requires a large number of simulations to be resolved. We next apply a reconstruction algorithm to recover some of the BAO signal that had been smeared by non-linear evolution, and we rerun the analysis. We find that after reconstruction, the rms error on the distance measurement improves by a factor of ~1.7 at low redshift (consistent with previous results), and the variance ({\sigma}^2) shows a change of up to 18% between optimal and sub-optimal cases (note, however, that these discrepancies may depend in detail on the procedure used to isolate the BAO signal). We finally discuss the impact of this work on current data analyses.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".