Satellites around Milky Way Analogs: Tension in the Number and Fraction of Quiescent Satellites Seen in Observations versus Simulations
Bibliographic record
Abstract
We compare the star-forming properties of satellites around Milky Way (MW) analogs from the Stage II release of the Satellites Around Galactic Analogs Survey (SAGA-ii) to those from the APOSTLE and Auriga cosmological zoom-in simulation suites. We use archival GALEX UV imaging as a star formation indicator for the SAGA-ii sample and derive star formation rates (SFRs) to compare with those from APOSTLE and Auriga. We compare our detection rates from the NUV and FUV bands to the SAGA-ii Hα detections and find that they are broadly consistent with over 85% of observed satellites detected in all three tracers. We apply the same spatial selection criteria used around SAGA-ii hosts to select satellites around the MW-like hosts in APOSTLE and Auriga. We find very good overall agreement in the derived SFRs for the star-forming satellites as well as the number of star-forming satellites per host in observed and simulated samples. However, the number and fraction of quenched satellites in the SAGA-ii sample are significantly lower than those in APOSTLE and Auriga below a stellar mass of M ∗ ∼ 108 M o ̇, even when the SAGA-ii incompleteness and interloper corrections are included. This discrepancy is robust with respect to the resolution of the simulations and persists when alternative star formation tracers are employed. We posit that this disagreement is not readily explained by vagaries in the observed or simulated samples considered here, suggesting a genuine discrepancy that may inform the physics of satellite populations around MW analogs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".