The quenched satellite population around Milky Way analogues
Bibliographic record
Abstract
ABSTRACT We study the relative fractions of quenched and star-forming satellite galaxies in the Satellites Around Galactic Analogs (SAGA) survey and Exploration of Local VolumE Satellites (ELVES) program, two nearby and complementary samples of Milky Way-like galaxies that take different approaches to identify faint satellite galaxy populations. We cross-check and validate sample cuts and selection criteria, as well as explore the effects of different star-formation definitions when determining the quenched satellite fraction of Milky Way analogues. We find the mean ELVES quenched fraction (〈QF〉), derived using a specific star formation rate (sSFR) threshold, decreases from ∼50 per cent to ∼27 per cent after applying a cut in absolute magnitude to match that of the SAGA survey (〈QF〉SAGA ∼9 per cent). We show these results are consistent for alternative star-formation definitions. Furthermore, these quenched fractions remain virtually unchanged after applying an additional cut in surface brightness. Using a consistently derived sSFR and absolute magnitude limit for both samples, we show that the quenched fraction and the cumulative number of satellites in the ELVES and SAGA samples broadly agree. We briefly explore radial trends in the ELVES and SAGA samples, finding general agreement in the number of star-forming satellites per host as a function of radius. Despite the broad agreement between the ELVES and SAGA samples, some tension remains with these quenched fractions in comparison to the Local Group and simulations of Milky Way analogues.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".