Cosmological constraints from baryon acoustic oscillations and clustering of large-scale structure
Bibliographic record
Abstract
We constrain cosmological parameters using combined measurements of the baryon acoustic oscillation (BAO) feature in the correlation function of galaxies and Ly-\alpha absorbers that together cover 0.1 < z < 2.4. The BAO position measurements alone -- without fixing the absolute sound horizon `standard ruler' length with cosmic microwave background (CMB) data -- constrain \Omega_m = 0.303 +/- 0.040 (68 per cent confidence) for a flat \Lambda CDM model, and w = -1.06^{+0.33}_{-0.32}, \Omega_m = 0.292^{+0.045}_{-0.040} for a flat wCDM model. Adding other large-scale structure (LSS) clustering constraints -- correlation function shape, the Alcock-Paczynski test and growth rate information -- to the BAO considerably tightens constraints (\Omega_m = 0.290 +/- 0.019, H_0 = 67.5 +/- 2.8 km s^{-1} Mpc^{-1}, \sigma_8 = 0.80 +/- 0.05 for \Lambda CDM, and w = -1.14 +/- 0.19 for wCDM). The LSS data mildly prefer a lower value of H_0, and a higher value of \Omega_m, than local distance ladder and type IA supernovae (SNe) measurements, respectively. While tension in the combined CMB, SNe and distance ladder data appears to be relieved by allowing w < -1, this freedom introduces tension with the LSS \sigma_8 constraint from the growth rate of matter fluctuations. The combined constraint on w from CMB, BAO and LSS clustering for a flat wCDM model is w = -1.03 +/- 0.06.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".