Are Gas-rich Ultra-diffuse Galaxies and Field Dwarfs Distinct?
Bibliographic record
Abstract
Abstract We explore the differences in gas-rich field ultra-diffuse galaxies (UDGs) and diffuse classical dwarf galaxies using an extensive atomic gas (H I ) follow-up survey of optically selected UDG candidates from the Systematically Measuring Ultra-diffuse Galaxies (SMUDGes) catalogue. We also compare the SMUDGes-H I observations with two state-of-the-art cosmological hydrodynamical simulations: Numerical Investigation of a Hundred Astrophysical Objects (NIHAO), where UDGs form through a series of bursty star formation episodes and R omulus 25, where UDGs form as a result of major mergers that temporarily increase their spin. Although the suggested formation scenarios for UDGs within these simulations are different, the present-day H I masses M H I , stellar masses M * , and star formation rates of simulated galaxies are qualitatively and quantitatively consistent with each other and with the observed SMUDGes-H I sample. We find that when controlling for M * , there is a positive correlation between the gas richness M H I / M * and the effective optical radius R eff , and that this trend is not different between the UDG and dwarf populations, within the measured scatter. Taken together, our results suggest that gas-rich, star-forming UDGs and dwarfs are not distinct galaxy populations, either observationally or in simulations.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".