THE DISTRIBUTION OF FAINT SATELLITES AROUND CENTRAL GALAXIES IN THE CANADA-FRANCE-HAWAII TELESCOPE LEGACY SURVEY
Bibliographic record
Abstract
We investigate the radial number density profile and the abundance distribution of faint satellites around central galaxies in the low-redshift universe using the Canada–France–Hawaii Telescope (CFHT) Legacy Survey. We consider three samples of central galaxies with magnitudes of M r = −21, −22, and −23 selected from the Sloan Digital Sky Survey group catalog of Yang et al. The satellite distribution around these central galaxies is obtained by cross-correlating these galaxies with the photometric catalog of the CFHT Legacy Survey. The projected radial number density of the satellites obeys a power-law form with the best-fit logarithmic slope of −1.05, independent of both the central galaxy luminosity and the satellite luminosity. The projected cross-correlation function between central and satellite galaxies exhibits a non-monotonic trend with satellite luminosity. It is most pronounced for central galaxies with M r = −21, where the decreasing trend of clustering amplitude with satellite luminosity is reversed when satellites are fainter than central galaxies by more than 2 mag. A comparison with the satellite luminosity functions in the Milky Way (MW) and M31 shows that the MW/M31 system has about twice as many satellites as around a typical central galaxy of similar luminosity. The implications for theoretical models are briefly discussed.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".