The 2dF Galaxy Redshift Survey: hierarchical galaxy clustering
Bibliographic record
Abstract
We use the Two-Degree Field Galaxy Redshift Survey (2dFGRS) to test the hierarchical scaling hypothesis: namely, that the p-point galaxy correlation functions can be written in terms of the two-point correlation function or variance. This scaling is expected if an initially Gaussian distribution of density fluctuations evolves under the action of gravitational instability. We measure the volume-averaged p-point correlation functions using a counts-in-cells technique applied to a volume-limited sample of 44 931 L* galaxies. We demonstrate that L* galaxies display hierarchical clustering up to order p= 6 in redshift space. The variance measured for L* galaxies is in excellent agreement with the predictions from a Λ-cold dark matter N-body simulation. This applies to all cell radii considered, 0.3 < (R/h−1 Mpc) < 30. However, the higher order correlation functions of L* galaxies have a significantly smaller amplitude than is predicted for the dark matter for R < 10 h−1 Mpc. This disagreement implies that a non-linear bias exists between the dark matter and L* galaxies on these scales. We also show that the presence of two rare, massive superclusters in the 2dFGRS has an impact on the higher-order clustering moments measured on large scales.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
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".