Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). III. The Southern SMUDGes Catalog
Bibliographic record
Abstract
Abstract We present a catalog of 5598 ultra-diffuse galaxy (UDG) candidates with effective radius r e > 5.″3 distributed throughout the southern portion of the DESI Legacy Imaging Survey covering ∼15,000 deg 2 . The catalog is most complete for physically large ( r e > 2.5 kpc) UDGs lying in the redshift range 1800 ≲ cz /km s −1 ≲ 7000, where the lower bound is defined by where incompleteness becomes significant for large objects on the sky and the upper bound by our minimum angular size selection criterion. Because physical size is integral to the definition of a UDG, we develop a method of distance estimation using existing redshift surveys. With three different galaxy samples, two of which contain UDGs with spectroscopic redshifts, we estimate that the method has a redshift accuracy of ∼75% when the method converges, although larger, more representative spectroscopic UDG samples are needed in order to fully understand the behavior of the method. We are able to estimate distances for 1079 of our UDG candidates (19%). Finally, to illustrate some uses of the catalog, we present both distance-independent and distance-dependent results. In the latter category, we establish that the red sequence of UDGs lies on the extrapolation of the red sequence relation for bright ellipticals and that the environment–color relation is at least qualitatively similar to that of high surface brightness galaxies. Both of these results challenge some of the models proposed for UDG evolution.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".