Quantifying the abundance of faint, low-redshift satellite galaxies in the COSMOS survey
Bibliographic record
Abstract
Faint dwarf satellite galaxies are important as tracers of small-scale structure, but remain poorly characterized outside the Local Group, due to the difficulty of identifying them consistently at larger distances. We review a recently proposed method for estimating the average satellite population around a given sample of nearby bright galaxies, using a combination of size and magnitude cuts (to select low-redshift dwarf galaxies preferentially) and clustering measurements (to estimate the fraction of true satellites in the cut sample). We test this method using the high-precision photometric redshift catalogue of the COSMOS survey, exploring the effect of specific cuts on the clustering signal. The most effective of the size-magnitude cuts considered recover the clustering signal around low-redshift primaries (z < 0.15) with about two-thirds of the signal and 80 per cent of the signal-to-noise ratio obtainable using the full COSMOS photometric redshifts. These cuts are also fairly efficient, with more than one-third of the selected objects being clustered satellites. We conclude that structural selection represents a useful tool in characterizing dwarf populations to fainter magnitudes and/or over larger areas than are feasible with spectroscopic surveys. In reviewing the low-redshift content of the COSMOS field, we also note the existence of several dozen objects that appear resolved or partially resolved in the HST imaging, and are confirmed to be local (at distances of ∼250 Mpc or less) by their photometric or spectroscopic redshifts. This underlines the potential for future space-based surveys to reveal local populations of intrinsically faint galaxies through imaging alone.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".