Star formation characteristics of CNN-identified post-mergers in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS)

• Published in: arXiv.org 2022-05
• Main Authors: Bickley, Robert W, Ellison, Sara L, Patton, David R, Connor Bottrell, Gwyn, Stephen, Hudson, Michael J
• Format: Article
• Language: English
• Subjects: Acquisitions & mergers; Artificial neural networks; Galactic evolution; Galaxies; Inspection; Red shift; Star & galaxy formation; Star formation rate; Stars & galaxies; Statistical methods; Unions
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2205.14103

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 present the characteristics of the galaxies with the highest CNN-predicted post-merger certainties, as well as a visually confirmed subset of 699 post-mergers. We find that post-mergers with high CNN merger probabilities (p(x)>0.8) have an average star formation rate that is 0.1 dex higher than a mass- and redshift-matched control sample. The SFR enhancement is even greater in the visually confirmed post-merger sample, a factor of two higher than the control sample.