A hierarchical clustering method for quantifying satellite abundance
Bibliographic record
Abstract
ABSTRACT We present a new method for quantifying the abundance of satellites around field galaxies and in groups. The method is designed to work with samples such as photometric redshift catalogues, that do not have full spectroscopic coverage, but for which some redshift or distance information is available. It consists of identifying the galaxies most likely to be centrals, and using the clustering signal around them as a template to iteratively decompose the full population into satellite and central populations. Thus, it is similar to performing crowded-field photometry by using isolated stars to determine the point spread function of the image. The method does not identify individual satellites or centrals conclusively, but assigns a probability to each galaxy of being one or the other. Averaged over a large sample, it provides a statistical estimate of satellite abundance, even in crowded fields with large redshift uncertainties. We test the method using data from the COSMOS field, which includes a large set of local objects with accurate photometric redshifts. We measure satellite abundance as a function of central stellar or halo mass, as well as the satellite luminosity function, and find results consistent with previous studies, but extending over a broader range of central masses. We also consider a number of possible systematic uncertainties in the method, and show that they are generally smaller than our random errors. Having presented the method in this paper, we will use it to study the properties of the satellite populations in a forthcoming one.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".