  Interactions between galaxies leave distinguishable imprints in the form of
tidal features which hold important clues about their mass assembly.
Unfortunately, these structures are difficult to detect because they are low
surface brightness features so deep observations are needed. Upcoming surveys
promise several orders of magnitude increase in depth and sky coverage, for
which automated methods for tidal feature detection will become mandatory. We
test the ability of a convolutional neural network to reproduce human visual
classifications for tidal detections. We use as training $\sim$6000 simulated
images classified by professional astronomers. The mock Hyper Suprime Cam
Subaru (HSC) images include variations with redshift, projection angle and
surface brightness ($\mu_{lim}$ =26-35 mag arcsec$^{-2}$). We obtain
satisfactory results with accuracy, precision and recall values of Acc=0.84,
P=0.72 and R=0.85, respectively, for the test sample. While the accuracy and
precision values are roughly constant for all surface brightness, the recall
(completeness) is significantly affected by image depth. The recovery rate
shows strong dependence on the type of tidal features: we recover all the
images showing shell features and 87% of the tidal streams; these fractions are
below 75% for mergers, tidal tails and bridges. When applied to real HSC
images, the performance of the model worsens significantly. We speculate that
this is due to the lack of realism of the simulations and take it as a warning
on applying deep learning models to different data domains without prior
testing on the actual data.
