Ultra diffuse galaxies in the MATLAS low-to-moderate density fields
Bibliographic record
Abstract
Recent advances in deep dedicated imaging surveys over the past decade have uncovered a surprisingly large number of extremely faint low surface brightness galaxies with large physical sizes called ultra diffuse galaxies (UDGs) in clusters and, more recently, in lower density environments. As part of the Mass Assembly of early-Type GaLAxies with their fine Structures (MATLAS) survey, a deep imaging large program at the Canada-France-Hawaii Telescope (CFHT), our team has identified 2210 dwarf galaxies, 59 (∼3%) of which qualify as UDGs. Averaging over the survey area, we find ∼0.4 UDG per square degree. They are found in a range of low to moderate density environments, although 61% of the sample fall within the virial radii of groups. Based on a detailed analysis of their photometric and structural properties, we find that the MATLAS UDGs do not show significant differences from the traditional dwarfs, except from the predefined size and surface brightness cut. Their median color is as red as the one measured in galaxy clusters, albeit with a narrower color range. The majority of the UDGs are visually classified as dwarf ellipticals with log stellar masses of ∼6.5 − 8.7. The fraction of nucleated UDGs (∼34%) is roughly the same as the nucleated fraction of the traditional dwarfs. Only five (∼8%) UDGs show signs of tidal disruption and only two are tidal dwarf galaxy candidates. A study of globular cluster (GC) candidates selected in the CFHT images finds no evidence of a higher GC specific frequency S N for UDGs than for classical dwarfs, contrary to what is found in most clusters. The UDG halo-to-stellar mass ratio distribution, as estimated from the GC counts, peaks at roughly the same value as for the traditional dwarfs, but spans the smaller range of ∼10 − 2000. We interpret these results to mean that the large majority of the field-to-group UDGs do not have a different formation scenario than traditional dwarfs.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".