The hierarchical clustering method: abundance and properties of local satellite populations
Bibliographic record
Abstract
ABSTRACT The faint satellites of the local Universe provide an important benchmark for our understanding of structure formation and galaxy formation, but satellite populations are hard to identify beyond the Local Group. We recently developed an iterative method to quantify satellite abundance using galaxy clustering and tested it on a local sample in the COSMOS field, where accurate photometric redshifts are available for a large number of faint objects. In this paper, we consider the properties of these satellite populations in more detail, studying the satellite stellar mass function (SSMF), the satellite-central connection, and quenching as a function of satellite and central mass and colour. Despite the limited sample size, our results show good consistency with those from much larger surveys and constrain the SSMF down to some of the lowest primary masses considered to date. We reproduce several known trends in satellite abundance and quenching, and find evidence for one new one, a dependence of the quiescent fraction on the primary-to-secondary halo mass ratio. We discuss the prospects for the clustering method in current and forthcoming surveys.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".