Systematically Measuring Ultra-diffuse Galaxies (SMUDGes). II. Expanded Survey Description and the Stripe 82 Catalog
Bibliographic record
Abstract
Abstract We present 226 large ultra-diffuse galaxy (UDG) candidates (r e > 5.″3, μ 0,g > 24 mag arcsec−2) in the SDSS Stripe 82 region recovered using our improved procedure developed in anticipation of processing the entire Legacy Surveys footprint. The advancements include less constrained structural parameter fitting, expanded wavelet filtering criteria, consideration of Galactic dust, estimates of parameter uncertainties and completeness based on simulated sources, and refinements of our automated candidate classification. We have a sensitivity ∼1 mag fainter in μ 0,g than the largest published catalog of this region. Using our completeness-corrected sample, we find that (1) there is no significant decline in the number of UDG candidates as a function of μ 0,g to the limit of our survey (∼26.5 mag arcsec−2); (2) bluer candidates have smaller Sérsic n; (3) most blue (g–r < 0.45 mag) candidates have μ 0,g ≲ 25 mag arcsec−2 and will fade to populate the UDG red sequence we observe to ∼26.5 mag arcsec−2; (4) any red UDGs that exist significantly below our μ 0,g sensitivity limit are not descendent from blue UDGs in our sample; and (5) candidates with lower μ 0,g tend to smaller n. We anticipate that the final SMUDGes sample will contain ∼30 × as many candidates.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".