Star formation characteristics of CNN-identified post-mergers in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS)
Robert W. Bickley, Sara L. Ellison, David R. Patton, Connor Bottrell,, Stephen Gwyn, Michael J. Hudson

TL;DR
This study uses a CNN trained on simulated galaxy data to identify post-mergers in the UNIONS survey, revealing that high-probability post-mergers exhibit significantly enhanced star formation rates compared to controls.
Contribution
It introduces a CNN-based method for identifying post-mergers in large survey data, improving detection efficiency over visual inspection and statistical methods.
Findings
High CNN-predicted post-mergers have 0.1 dex higher SFR than controls.
Visually confirmed post-mergers show twice the SFR enhancement.
CNN effectively distinguishes post-mergers in survey images.
Abstract
The importance of the post-merger epoch in galaxy evolution has been well-documented, but post-mergers are notoriously difficult to identify. While the features induced by mergers can sometimes be distinctive, they are frequently missed by visual inspection. In addition, visual classification efforts are highly inefficient because of the inherent rarity of post-mergers (~1% in the low-redshift Universe), and non-parametric statistical merger selection methods do not account for the diversity of post-mergers or the environments in which they appear. To address these issues, we deploy a convolutional neural network (CNN) which has been trained and evaluated on realistic mock observations of simulated galaxies from the IllustrisTNG simulations, to galaxy images from the Canada France Imaging Survey (CFIS), which is part of the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). We…
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