Detection significance of baryon acoustic oscillations peaks in galaxy and quasar clustering
Bibliographic record
Abstract
We compare our analysis of the baryon acoustic oscillations (BAO) feature in the correlation functions of SDSS BOSS DR12 LOWZ and CMASS galaxy samples with previous literature results.Using subsets of the data we obtain an empirical estimate of the errors on the correlation functions that are in agreement with the simulated errors of previous works.We find that the significance of BAO detection is the quantity most sensitive to the choice of the fitting range with the CMASS value decreasing from 8.0σ to 5.3σ as the fitting range is reduced.Although our measurements of D V (z) are in agreement with previous studies, we note that their CMASS 8.0σ (LOWZ 4.0σ ) detection significance reduces to 4.7σ (2.8σ ) in fits with their diagonal covariance terms only.We extend our BAO analysis to higher redshifts by fitting to the weighted mean of 2QDESp, SDSS DR5 UNIFORM, 2QZ, and 2SLAQ quasar correlation functions, obtaining a 7.6 per cent measurement compared to 3.9 per cent achieved by eBOSS DR14.Unlike for the LRG surveys, the larger error on quasar correlation functions implies a smaller role for nuisance parameters (accounting for scale-dependent clustering) in providing a good fit to the fiducial cold dark matter model.Again we find that the eBOSS peak significance reduces from 2.8 to 1.4σ if we ignore the off-diagonal covariance matrix terms in our fitting.We conclude that for both LRGs and quasars, the reported BAO peak significances from the SDSS surveys depend sensitively on the accuracy of the covariance matrix at large separations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".