The Next Generation Virgo Cluster Survey (NGVS). XL. The Morphological Classification of Virgo Cluster Galaxies
Bibliographic record
Abstract
Abstract We present a study of morphologies, based on deep u * g ′ i ′ z ′ imaging of the Virgo Cluster from the Next Generation Virgo Cluster Survey, for 3689 Virgo cluster members spanning a mass range of ∼10 11 –10 5 M ⊙ . Our analysis introduces a new, two-component visual classification scheme developed to capture the morphological diversity of galaxies over more than 6 orders of magnitude in stellar mass. Our morphological classifications use two parameters to describe the global structure and star formation activity of each galaxy. Structural subcodes denote features such as spiral arms, bars, disks, shells, and streams, while star formation subcodes indicate the form and location of the current star formation activity (e.g., in cores, clumps, filaments). These visual classifications rely on deep g ′ -band images, supplemented by u * g ′ i ′ color images, as well as unsharp-masked images for a subset of objects. We compare our classifications to previous results for bright member galaxies that used more established schemes, finding good agreement. We also measure quantitative classification statistics (e.g., CASGM 20 ) for a subset of the brighter galaxies, and present catalogs for some galaxy types of special interest, including structurally compact galaxies, ultradiffuse galaxies, candidate ultracompact dwarf transition objects, as well as candidate postmerger systems. These morphological classifications may be useful as a training set in the application of machine learning tools to the next generation of wide-field imaging surveys.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".