WALLABY Pilot Survey: kNN identification of perturbed galaxies through HI morphometrics
Bibliographic record
Abstract
Galaxy morphology in stellar light can be described by a series of "non-parametric" or "morphometric" parameters, such as concentration-asymmetry-smoothness, Gini, $M_{20}$, and Sersic fit. These parameters can be applied to column density maps of atomic hydrogen (HI). The HI distribution is susceptible to perturbations by environmental effects, e.g. inter-galactic medium pressure and tidal interactions. Therefore, HI morphology can potentially identify galaxies undergoing ram-pressure stripping or tidal interactions. We explore three fields in the WALLABY Pilot HI survey and identify perturbed galaxies based on a k-nearest Neighbor (kNN) algorithm using an HI morphometric feature space. For training, we used labeled galaxies in the combined NGC 4808 and NGC 4636 fields with six HI morphometrics to train and test a kNN classifier. The kNN classification is proficient in classifying perturbed galaxies with all metrics -- accuracy, precision and recall -- at 70-80%. By using the kNN method to identify perturbed galaxies in the deployment field, the NGC 5044 mosaic, we find that in most regards, the scaling relations of perturbed and unperturbed galaxies have similar distribution in the scaling relations of stellar mass vs star formation rate and the Baryonic Tully-Fisher relation, but the HI and stellar mass relation flatter than of the unperturbed galaxies. Our results for NGC 5044 provide a prediction for future studies on the fraction of galaxies undergoing interaction in this catalogue and to build a training sample to classify such galaxies in the full WALLABY survey.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".