The 6dF Galaxy Survey: cosmological constraints from the velocity power spectrum
Bibliographic record
Abstract
We present scale-dependent measurements of the normalized growth rate of structure fσ8(k, z = 0) using only the peculiar motions of galaxies. We use data from the 6-degree Field Galaxy Survey velocity sample together with a newly compiled sample of low-redshift (z < 0.07) Type Ia supernovae. We constrain the growth rate in a series of Δk ∼ 0.03 h Mpc−1 bins to ∼35 per cent precision, including a measurement on scales >300 h−1 Mpc, which represents one of the largest scale growth rate measurement to date. We find no evidence for a scale-dependence in the growth rate, or any statistically significant variation from the growth rate as predicted by the Planck cosmology. Bringing all the scales together, we determine the normalized growth rate at z = 0 to ∼15 per cent in a manner independent of galaxy bias and in excellent agreement with the constraint from the measurements of redshift-space distortions from 6-degree Field Galaxy Survey. We pay particular attention to systematic errors. We point out that the intrinsic scatter present in Fundamental Plane and Tully–Fisher relations is only Gaussian in logarithmic distance units; wrongly assuming it is Gaussian in linear (velocity) units can bias cosmological constraints. We also analytically marginalize over zero-point errors in distance indicators, validate the accuracy of all our constraints using numerical simulations, and demonstrate how to combine different (correlated) velocity surveys using a matrix ‘hyperparameter’ analysis. Current and forthcoming peculiar velocity surveys will allow us to understand in detail the growth of structure in the low-redshift universe, providing strong constraints on the nature of dark energy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".