Gas and Star Formation in Satellites of Milky Way Analogs
Bibliographic record
Abstract
Abstract We have imaged the entirety of eight (plus one partial) Milky Way (MW)–like satellite systems, a total of 42 (45) satellites, from the Satellites Around Galactic Analogs II catalog in both Hα and H i with the Canada–France–Hawaii Telescope and the Jansky Very Large Array. In these eight systems we have identified four cases where a satellite appears to be currently undergoing ram pressure stripping (RPS) as its H i gas collides with the circumgalactic medium (CGM) of its host. We also see a clear suppression of gas fraction (M HI/M *) with decreasing (projected) satellite–host separation—to our knowledge, the first time this has been observed in a sample of MW-like systems. Comparisons to the Auriga, A Project Of Simulating The Local Environment, and TNG50 cosmological zoom-in simulations show consistent global behavior, but they systematically underpredict gas fractions across all satellites by roughly 0.5 dex. Using a simplistic RPS model, we estimate the average peak CGM density that satellites in these systems have encountered to be log ρ cgm / g cm − 3 ≈ − 27.3 . Furthermore, we see tentative evidence that these satellites are following a specific star formation rate to gas fraction relation that is distinct from field galaxies. Finally, we detect one new gas-rich satellite in the UGC 903 system with an optical size and surface brightness meeting the standard criteria to be considered an ultra-diffuse galaxy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".