Baryon acoustic oscillations from the complete SDSS-III Ly<i>α</i>-quasar cross-correlation function at z = 2.4
Bibliographic record
Abstract
We present a measurement of baryon acoustic oscillations (BAO) in the cross-correlation of quasars with the Lyα-forest flux transmission at a mean redshift ofz= 2.40. The measurement uses the complete Sloan Digital Sky Survey (SDSS-III) data sample: 168 889 forests and 234 367 quasars from the SDSS data release DR12. In addition to the statistical improvement on our previous study using DR11, we have implemented numerous improvements at the analysis level enabling a more accurate measurement of this cross-correlation. We have also developed the first simulations of the cross-correlation that allow us to test different aspects of our data analysis and to search for potential systematic errors in the determination of the BAO peak position. We measure the two ratiosDH(z= 2.40) /rd= 9.01 ± 0.36 andDM(z= 2.40) /rd= 35.7 ± 1.7, where the errors include marginalization over the non-linear velocity of quasars and the cross-correlation of metals and quasars, among other effects. These results are within 1.8σof the prediction of the flat-ΛCDM model describing the observed cosmic microwave background anisotropies. We combine this study with the Lyα-forest auto-correlation function, yieldingDH(z= 2.40) /rd= 8.94 ± 0.22 andDM(z= 2.40) /rd= 36.6 ± 1.2, within 2.3σof the same flat-ΛCDM model.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".