WALLABY Pilot Survey: hydra cluster galaxies UV and H <scp>i</scp>morphometrics
Bibliographic record
Abstract
ABSTRACT Galaxy morphology in atomic hydrogen (H i) and in the ultraviolet (UV) are closely linked. This has motivated their combined use to quantify morphology over the full H i disc for both H i and UV imaging. We apply galaxy morphometrics: concentration, asymmetry, gini, M20 and multimode-intensity-deviation statistics to the first moment-0 maps of the WALLABY Survey of galaxies in the hydra cluster centre. Taking advantage of this new H i survey, we apply the same morphometrics over the full H i extent on archival GALEX FUV and NUV data to explore how well H i truncated, extended ultraviolet disc (XUV) and other morphological phenomena can be captured using pipeline WALLABY data products. Extended H i and UV discs can be identified relatively straightforward from their respective concentration. Combined with WALLABY H i, even the shallowest GALEX data are sufficient to identify XUV discs. Our second goal is to isolate galaxies undergoing ram-pressure stripping in the H i morphometric space. We employ four different machine learning techniques, a decision tree, a k-nearest neighbour, a support-vector machine, and a random forest. Up to 80 per cent precision and recall are possible with the random forest giving the most robust results.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".