Bibliographic record
Abstract
The number and distribution of dwarf satellite galaxies remain a critical test of cold dark matter–dominated structure formation on small scales. Until recently, observational information about galaxy formation on these scales has been limited mainly to the Local Group. We have searched for faint analogues of Local Group dwarfs around nearby bright galaxies, using a spatial clustering analysis of the photometric catalog of the Sloan Digital Sky Survey (SDSS) Data Release 8. Several other recent searches of SDSS have detected clustered satellite populations down to Δ m r ≡ ( m r , sat − m r , main ) ∼ 6–8, using photometric redshifts to reduce background contamination. SDSS photometric redshifts are relatively imprecise, however, for faint and nearby galaxies. Instead, we use angular size to select potential nearby dwarfs and consider only the nearest isolated bright galaxies as primaries. As a result, we are able to detect an excess clustering signal from companions down to Δ m r = 12, 4 mag fainter than most recent studies. We detect an overdensity of objects at separations <400 kpc, corresponding to about 4.6 ± 0.5 satellites per central galaxy, consistent with the satellite abundance expected from the Local Group, given our selection function. Although the sample of satellites detected is incomplete by construction, since it excludes the least and most compact dwarfs, this detection provides a lower bound on the average satellite luminosity function, down to luminosities corresponding to the faintest "classical" dwarfs of the Local Group.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".