Quantified HI Morphology I: Multi-Wavelengths Analysis of the THINGS Galaxies
B. W. Holwerda (1,2), N. Pirzkal (3), W.J.G. de Blok (2), A. Bouchard, (4), S-L. Blyth (2), K. J. van der Heyden (2), E. C. Elson (2) ((1), European Space Agency, ESTEC, (2) Astrophysics, Cosmology, Gravity Centre, (ACGC), Astronomy Department, University of Cape Town

TL;DR
This study analyzes galaxy morphology across multiple wavelengths, especially HI gas, to identify indicators of galaxy interactions, finding that certain parameters like Asymmetry and M20 are effective in 21 cm observations.
Contribution
It introduces a multi-wavelength morphological analysis of galaxies, emphasizing HI gas as a new indicator of interactions, and evaluates the effectiveness of various parameters in this context.
Findings
HI morphology is as effective as other wavelengths in detecting interactions.
Asymmetry and M20 are promising parameters for HI-based interaction tracing.
Wavelength choice impacts morphological parameter effectiveness less than the area used.
Abstract
Galaxy evolution is driven to a large extent by interactions and mergers with other galaxies and the gas in galaxies is extremely sensitive to the interactions. One method to measure such interactions uses the quantified morphology of galaxy images. Well-established parameters are Concentration, Asymmetry, Smoothness, Gini, and M20 of a galaxy image. Thus far, the application of this technique has mostly been restricted to restframe ultra-violet and optical images. However, with the new radio observatories being commissioned (MeerKAT, ASKAP, EVLA, WSRT/APERTIF, and ultimately SKA), a new window on the neutral atomic hydrogen gas (HI) morphology of a large numbers of galaxies will open up. The quantified morphology of gas disks of spirals can be an alternative indicator of the level and frequency of interaction. The HI in galaxies is typically spatially more extended and more sensitive…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
