Galaxy groups in the 2dF Galaxy Redshift Survey: the number density of groups
Bibliographic record
Abstract
The abundance of galaxy clusters as a function of mass is determined from the 2dF Galaxy Redshift Survey (2dFGRS) Percolation-Inferred Galaxy Group (2PIGG) catalogue. This is used to estimate the amplitude of the matter fluctuation spectrum, parametrized by the linear theory rms density fluctuations in spheres of radius 8h−1 Mpc, σ8. The best-fitting value for this parameter is highly correlated with the mean matter density in the Universe, Ωm, and is found to satisfy σ8= 0.25 Ω −0.92-4.5(Ω m−0.22) 2m± 10 per cent (statistical) ±20 per cent (systematic) for 0.18 ≤Ωm≤ 0.50, assuming that Ωm+ΩΛ= 1. This gives σ8= 0.89 when evaluated at Ωm= 0.25. A ∼20 per cent correction has been applied to undo the systematic bias inherent in the measurement procedure. Mock catalogues, constructed from large cosmological N-body simulations, are used to help understand and model these systematic errors. The abundance of galaxy groups as a function of group bJ-band luminosity is also determined. This is used in conjunction with the halo mass function, determined from simulations, to infer the variation of halo mass-to-light ratio over four orders of magnitude in halo mass. The mass-to-light ratio shows a minimum value of 100 h M⊙/L⊙ in the bJ band at a total group luminosity of L b J≈ 5×109h−2 L⊙. Together with the observed Tully–Fisher (TF) relation, this implies that the observed rotation speed of TF galaxies with L b J≤ 1010h−2 L⊙ is within ∼10 per cent of the typical circular speed of the haloes that host them.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".