The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: single-probe measurements from CMASS anisotropic galaxy clustering
Bibliographic record
Abstract
With the largest spectroscopic galaxy survey volume drawn from the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS), we can extract cosmological constraints from the measurements of redshift and geometric distortions at quasi-linear scales (e.g. above 50 h -1 Mpc). We analyse the broad-range shape of the monopole and quadrupole correlation functions of the BOSS Data Release 12 (DR12) CMASS galaxy sample, at the effective redshift z = 0.59, to obtain constraints on the Hubble expansion rate H(z), the angular-diameter distance D A (z), the normalized growth rate f(z) 8 (z), and the physical matter density m h 2 . We obtain robust measurements by including a polynomial as the model for the systematic errors, and find it works very well against the systematic effects, e.g. ones induced by stars and seeing. We provide accurate measurements {D A (0.59)r s,fid /r s , H(0.59)r s /r s,fid , f(0.59) 8 (0.59), m h 2 } = {1427 26 Mpc, 97.3 3.3 km s -1 Mpc -1 , 0.488 0.060, 0.135 0.016}, where r s is the comoving sound horizon at the drag epoch and r s,fid = 147.66 Mpc is the sound scale of the fiducial cosmology used in this study. The parameters which are not well constrained by our galaxy clustering analysis are marginalized over with wide flat priors. Since no priors from other data sets, e.g. cosmic microwave background (CMB), are adopted and no dark energy models are assumed, our results from BOSS CMASS galaxy clustering alone may be combined with other data sets, i.e. CMB, SNe, lensing or other galaxy clustering data to constrain the parameters of a given cosmological model. The uncertainty on the dark energy equation of state parameter, w, from CMB+CMASS is about 8 per cent. The uncertainty on the curvature fraction, k , is 0.3 per cent. We do not find deviation from flat CDM.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".