Systematically Measuring Ultradiffuse Galaxies. VII. The H i Survey Overview
Bibliographic record
Abstract
Abstract We present the results from the neutral hydrogen (H i) follow-up survey of 378 optically detected ultradiffuse galaxy (UDG) candidates from the Systematically Measuring Ultra-Diffuse Galaxies survey using the Robert C. Byrd Green Bank Telescope. We detect H i in 110 targets and determine 37 to be UDGs and 73 to be low-surface-brightness (LSB) dwarfs based on their effective radii and central surface brightnesses. In line with previous studies, we find that: (i) our H i detections have on average bluer g − r colors and more irregular morphologies than our H i nondetections, (ii) our H i detections populate the tail end of the star-forming main sequence from the ALFALFA catalog with marginally lower specific star formation rates, and (iii) H i detections are mostly in relatively isolated (i.e., field) environments, while most nondetections have at least one nearby neighbor in projection. We find that the H i mass to stellar mass ratios (i.e., gas richnesses) scale with the physical sizes for UDGs and LSB dwarfs alike, suggesting that mechanisms other than bursty star formation feedback may be at play for UDGs. However, we find a stronger trend between gas richnesses and physical sizes if we define UDGs using their effective surface brightness instead of their central surface brightness. We are in the process of using this unprecedented sample of UDG candidates to carry out detailed follow-up studies (i.e., star formation and environmental analysis, comparisons to simulations) and are obtaining resolved H i observations for several of them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".