  Merging and interactions can radically transform galaxies. However,
identifying these events based solely on structure is challenging as the status
of observed mergers is not easily accessible. Fortunately, cosmological
simulations are now able to produce more realistic galaxy morphologies,
allowing us to directly trace galaxy transformation throughout the merger
sequence. To advance the potential of observational analysis closer to what is
possible in simulations, we introduce a supervised deep learning Convolutional
Neural Network (CNN) and Vision Transformer (ViT) hybrid framework, Mummi
(MUlti Model Merger Identifier). Mummi is trained on realism-added synthetic
data from IllustrisTNG100-1, and is comprised of a multi-step ensemble of
models to identify mergers and non-mergers, and to subsequently classify the
mergers as interacting pairs or post-mergers. To train this ensemble of models,
we generate a large imaging dataset of 6.4 million images targeting UNIONS with
RealSimCFIS. We show that Mummi offers a significant improvement over many
previous machine learning classifiers, achieving 95% pure classifications even
at Gyr long timescales when using a jury-based decision making process,
mitigating class imbalance issues that arise when identifying real galaxy
mergers from $z=0$ to $0.3$. Additionally, we can divide the identified mergers
into pairs and post-mergers at 96% success rate. We drastically decrease the
false positive rate in galaxy merger samples by 75%. By applying Mummi to the
UNIONS DR5-SDSS DR7 overlap, we report a catalog of 13,448 high confidence
galaxy merger candidates. Finally, we demonstrate that Mummi produces powerful
representations solely using supervised learning, which can be used to bridge
galaxy morphologies in simulations and observations.
