  We take a deep learning-based approach for galaxy merger identification in
Subaru HSC-SSP, specifically through the use of deep representation learning
and fine-tuning, with the aim of creating a pure and complete merger sample
within the HSC-SSP survey. We can use this merger sample to conduct studies on
how mergers affect galaxy evolution. We use Zoobot, a deep learning
representation learning model pre-trained on citizen science votes on Galaxy
Zoo DeCALS images. We fine-tune Zoobot for the purpose of merger classification
of images of SDSS and GAMA galaxies in HSC-SSP PDR 3. Fine-tuning is done using
1200 synthetic HSC-SSP images of galaxies from the TNG simulation. We then find
merger probabilities on observed HSC images using the fine-tuned model. Using
our merger probabilities, we examine the relationship between merger activity
and environment. We find that our fine-tuned model returns an accuracy on the
synthetic validation data of 76%. This number is comparable to those of
previous studies where convolutional neural networks were trained with
simulation images, but with our work requiring a far smaller number of training
samples. For our synthetic data, our model is able to achieve completeness and
precision values of 80%. In addition, our model is able to correctly classify
both mergers and non-mergers of diverse morphologies and structures, including
those at various stages and mass ratios, while distinguishing between
projections and merger pairs. For the relation between galaxy mergers and
environment, we find two distinct trends. Using stellar mass overdensity
estimates for TNG simulations and observations using SDSS and GAMA, we find
that galaxies with higher merger scores favor lower density environments on
scales of 0.5 to 8 h^-1 Mpc. However, below these scales in the simulations, we
find that galaxies with higher merger scores favor higher density environments.
