The WiggleZ Dark Energy Survey: the selection function and z= 0.6 galaxy power spectrum
Bibliographic record
Abstract
We report one of the most accurate measurements of the three-dimensional large-scale galaxy power spectrum achieved to date, using 56 159 redshifts of bright emission-line galaxies at effective redshift z≈ 0.6 from the WiggleZ Dark Energy Survey at the Anglo-Australian Telescope. We describe in detail how we construct the survey selection function allowing for the varying target completeness and redshift completeness. We measure the total power with an accuracy of approximately 5 per cent in wavenumber bands of Δk= 0.01 h Mpc−1. A model power spectrum including non-linear corrections, combined with a linear galaxy bias factor and a simple model for redshift-space distortions, provides a good fit to our data for scales k < 0.4 h Mpc−1. The large-scale shape of the power spectrum is consistent with the best-fitting matter and baryon densities determined by observations of the cosmic microwave background radiation. By splitting the power spectrum measurement as a function of tangential and radial wavenumbers, we delineate the characteristic imprint of peculiar velocities. We use these to determine the growth rate of structure as a function of redshift in the range 0.4 < z < 0.8, including a data point at z= 0.78 with an accuracy of 20 per cent. Our growth rate measurements are a close match to the self-consistent prediction of the Λ cold dark matter model. The WiggleZ survey data will allow a wide range of investigations into the cosmological model, cosmic expansion and growth history, topology of cosmic structure and Gaussianity of the initial conditions. Our calculation of the survey selection function will be released at a future date via our website wigglez.swin.edu.au.
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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 | 0.001 |
| 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 | 0.000 | 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".