The WiggleZ Dark Energy Survey: improved distance measurements to z = 1 with reconstruction of the baryonic acoustic feature
Bibliographic record
Abstract
We present significant improvements in cosmic distance measurements from the WiggleZ Dark Energy Survey, achieved by applying the reconstruction of the baryonic acoustic feature technique. We show using both data and simulations that the reconstruction technique can often be effective despite patchiness of the survey, significant edge effects and shot-noise. We investigate three redshift bins in the redshift range 0.2 < z < 1, and in all three find improvement after reconstruction in the detection of the baryonic acoustic feature and its usage as a standard ruler. We measure model-independent distance measures DV(rfid s/rs) of 1716 ± 83, 2221 ± 101, 2516 ± 86 Mpc (68 per cent CL) at effective redshifts z = 0.44, 0.6, 0.73, respectively, where DV is the volume-averaged distance, and rs is the sound horizon at the end of the baryon drag epoch. These significantly improved 4.8, 4.5 and 3.4 per cent accuracy measurements are equivalent to those expected from surveys with up to 2.5 times the volume of WiggleZ without reconstruction applied. These measurements are fully consistent with cosmologies allowed by the analyses of the Planck Collaboration and the Sloan Digital Sky Survey. We provide the DV(rfid s/rs) posterior probability distributions and their covariances. When combining these measurements with temperature fluctuations measurements of Planck, the polarization of Wilkinson Microwave Anisotropy Probe 9, and the 6dF Galaxy Survey baryonic acoustic feature, we do not detect deviations from a flat Λ cold dark matter (ΛCDM) model. Assuming this model, we constrain the current expansion rate to H0 = 67.15 ± 0.98 km s-1Mpc-1. Allowing the equation of state of dark energy to vary, we obtain wDE =-1.080 ± 0.135. When assuming a curved ΛCDM model we obtain a curvature value of ΩK =-0.0043 ± 0.0047.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".