The Canada-France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ~5000 square degrees of the sky, representing a first-rate opportunity to identify recently-merged galaxies. Due to the large number of galaxies in CFIS, we investigate the use of a convolutional neural network (CNN) for automated merger classification. Training samples of post-merger and isolated galaxy images are generated from the IllustrisTNG simulation processed with the observational realism code RealSim. The CNN’s overall classification accuracy is 88 percent, remaining stable over a wide range of intrinsic and environmental parameters. We generate a mock galaxy survey from IllustrisTNG in order to explore the expected purity of post-merger samples identified by the CNN. Despite the CNN’s good performance in training, the intrinsic rarity of post-mergers leads to a sample that is only ~6 percent pure when the default decision threshold is used. We investigate trade-offs in purity and completeness with a variable decision threshold and find that we recover the statistical distribution of merger-induced star formation rate enhancements. Finally, the performance of the CNN is compared with both traditional automated methods and human classifiers. The CNN is shown to outperform Gini-M20 and asymmetry methods by an order of magnitude in post-merger sample purity on the mock survey data. Although the CNN outperforms the human classifiers on sample completeness, the purity of the post-merger sample identified by humans is frequently higher, indicating that a hybrid approach to classifications may be an effective solution to merger classifications in large surveys.

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 per cent 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.

Galaxy mergers trigger both star formation and accretion on to the central supermassive black hole. As a result of subsequent energetic feedback processes, it has long been proposed that star formation may be promptly extinguished in galaxy merger remnants. However, this prediction of widespread, rapid quenching in late stage mergers has been recently called into question with modern simulations and has never been tested observationally. Here we perform the first empirical assessment of the long-predicted end phase in the merger sequence. Based on a sample of ∼ 500 post-mergers identified from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS), we show that the frequency of post-merger galaxies that have rapidly shutdown their star formation following a previous starburst is 30-60 times higher than expected from a control sample of non-merging galaxies. No such excess is found in a sample of close galaxy pairs, demonstrating that mergers can indeed lead to a rapid halt to star formation, but that this process only manifests after coalescence.

In this work, we use ∼500 low-redshift (z ∼ 0.1) X-ray active galactic nuclei (AGNs) observed by XMM-Newton and the Sloan Digital Sky Survey (SDSS) to investigate the prevalence and nature of AGNs that apparently lack optical emission lines (“optically dull AGNs”). Although one quarter of spectra appear absorption-line dominated in visual assessment, line extraction with robust continuum subtraction from the MPA/JHU catalog reveals usable [O iii] measurements in 98% of the sample, allowing us to study [O iii]-underluminous AGNs together with more typical AGNs in the context of the L [O III]–L X relation. We find that “optically dull AGNs” do not constitute a distinct population of AGNs. Instead, they are the [O iii]-underluminous tail of a single, unimodal L [O III]–L X relation that has substantial scatter (0.6 dex). We find the degree to which an AGN is underluminous in [O iii] correlates with the specific star formation rate or D 4000 index of the host, which are both linked to the molecular gas fraction. Thus the emerging physical picture for the large scatter seems to involve the gas content of the narrow-line region. We find no significant role for previously proposed scenarios for the presence of optically dull AGNs, such as host dilution or dust obscuration. Despite occasionally weak lines in SDSS spectra, >80% of X-ray AGNs are identified as such with the Baldwin–Phillips–Terlevich diagram. More than 90% are classified as AGNs based only on [N ii]/Hα, providing more complete AGN samples when [O iii] or Hβ are weak. X-ray AGNs with LINER spectra obey essentially the same L [O III]–L X relation as Seyfert 2s, suggesting their line emission is produced by AGN activity.

Post-starburst (PSB) galaxies are defined as having experienced a recent burst of star formation, followed by a prompt truncation in further activity. Identifying the mechanism(s) causing a galaxy to experience a post-starburst phase therefore provides integral insight into the causes of rapid quenching. Galaxy mergers have long been proposed as a possible post-starburst trigger. Effectively testing this hypothesis requires a large spectroscopic galaxy survey to identify the rare PSBs as well as high quality imaging and robust morphology metrics to identify mergers. We bring together these critical elements by selecting PSBs from the overlap of the Sloan Digital Sky Survey and the Canada-France Imaging Survey and applying a suite of classification methods: non-parametric morphology metrics such as asymmetry and Gini-M20, a convolutional neural network trained to identify post-merger galaxies, and visual classification. This work is therefore the largest and most comprehensive assessment of the merger fraction of PSBs to date. We find that the merger fraction of PSBs ranges from 19 per cent to 42 per cent depending on the merger identification method and details of the PSB sample selection. These merger fractions represent an excess of 3-46× relative to non-PSB control samples. Our results demonstrate that mergers play a significant role in generating PSBs, but that other mechanisms are also required. However, applying our merger identification metrics to known post-mergers in the IllustrisTNG simulation shows that  70 per cent of recent post-mergers (≲200 Myr) would not be detected. Thus, we cannot exclude the possibility that nearly all post-starburst galaxies have undergone a merger in their recent past.