WALLABY Pilot Survey: kNN identification of perturbed galaxies through HI morphometrics
B.W. Holwerda (Louisville), Helga D\'enes (Yachay), J. Rhee (ICRAR),, D. Leahy (Calgary), B. Koribalski (ATNF), N. Yu (National Astronomical, Observatories, Bejing), N. Deg (Queen's University), T. Westmeier (ICRAR), K., Lee-Waddell (Australian SKA Regional Center)

TL;DR
This study uses a k-nearest Neighbor algorithm on HI morphometric parameters to identify perturbed galaxies in the WALLABY survey, achieving 70-80% accuracy, and compares their properties to unperturbed galaxies.
Contribution
It introduces a kNN-based method for identifying perturbed galaxies using HI morphometrics in large HI surveys, demonstrating its effectiveness.
Findings
kNN classifier achieves 70-80% accuracy in identifying perturbed galaxies
Perturbed and unperturbed galaxies show similar scaling relations in some properties
HI-stellar mass relation is flatter for perturbed galaxies
Abstract
Galaxy morphology in stellar light can be described by a series of "non-parametric" or "morphometric" parameters, such as concentration-asymmetry-smoothness, Gini, , 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…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Gamma-ray bursts and supernovae
