WALLABY Pilot Survey: hydra cluster galaxies UV and H i morphometrics. Issue 1 (28th February 2023)

• Record Type: Journal Article
• Authors: Holwerda, Benne W; Bigiel, Frank; Bosma, Albert; Courtois, Helene M; Deg, Nathan; Dénes, Helga; Elagali, Ahmed; For, Bi-Qing; Koribalski, Baerbel; Leahy, Denis A; Lee-Waddell, Karen; López-Sánchez, Ángel R; Oh, Se-Heon; Reynolds, Tristan N; Rhee, Jonghwan; Spekkens, Kristine; Wang, Jing; Westmeier, Tobias; Wong, O Ivy
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 521:Issue 1(2023)
• Page Start: 1502
• Page End: 1517
• Publication Date: 2023-02-28
• Subjects: galaxies: disc -- galaxies: ISM -- galaxies: kinematics and dynamics -- galaxies: spiral -- galaxies: statistics -- galaxies: structure; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stad602
• Languages: English
• ISSNs: 0035-8711

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, M 20 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.