The Optical Luminosity Function of Virialized Systems
Bibliographic record
Abstract
We determine the optical luminosity function of virialized systems over the full range of density enhancements, from single galaxies to clusters of galaxies. The analysis is based on galaxy systems identified from the Nearby Optical Galaxy (NOG) sample, which is the largest all-sky catalog of objectively identified bound objects presently available. We find that the B -band luminosity function of systems is insensitive to the choice of group-finding algorithms and is well described, over the absolute magnitude range -24.5 ≤ M - 5 log h 75 ≤ -18.5, by a Schechter function with α s = -1.4 ± 0.03, M - 5 log h 75 = -23.1 ± 0.06, and ϕ = 4.8 × 10 -4 h Mpc -3 , or by a double power law: ϕ pl ( L s ) ∝ L for L s < L pl and ϕ pl ( L s ) ∝ L for L s > L pl , with L pl = 8.5 × 10 10 h L ☉ , corresponding to M s - 5 log h 75 = -21.85. The characteristic luminosity of virialized systems, L pl , is ~3 times that ( L ) of the NOG galaxies. Our results show that half of the luminosity of the universe is generated in systems with L s < 2.9 L and that 10% of the overall luminosity density is supplied by systems with L s > 30 L . We find a significant environmental dependence in the luminosity function of systems, in the sense that overdense regions, as measured on scales of 5 h -1 Mpc, preferentially host brighter, and presumably more massive, virialized systems.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".