WALLABY Pilot Survey: kNN identification of perturbed galaxies through H <scp>1</scp> morphometrics
Bibliographic record
Abstract
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 Sérsic fit. These parameters can be applied to column density maps of atomic hydrogen (H 1 ). The H 1 distribution is susceptible to perturbations by environmental effects, for example, intergalactic medium pressure and tidal interactions. Therefore, H 1 morphology can potentially identify galaxies undergoing ram-pressure stripping or tidal interactions. We explore three fields in the WALLABY Pilot H 1 survey and identify perturbed galaxies based on a k-nearest neighbour (kNN) algorithm using an H 1 morphometric feature space. For training, we used labelled galaxies in the combined NGC 4808 and NGC 4636 fields with six H 1 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 versus star formation rate and the Baryonic Tully–Fisher relation, but the H 1 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".