  In a previous paper, we have introduced a deep learning neural network that
should be able to detect the existence of very shallow periodic planetary
transits in the presence of red noise. The network in that feasibility study
would not provide any further details about the detected transits. The current
paper completes this missing part. We present a neural network that tags
samples that were obtained during transits. This is essentially similar to the
task of identifying the semantic context of each pixel in an image -- an
important task in computer vision, called `semantic segmentation', which is
often performed by deep neural networks. The neural network we present makes
use of novel deep learning concepts such as U-Nets, Generative Adversarial
Networks (GAN), and adversarial loss. The resulting segmentation should allow
further studies of the light curves which are tagged as containing transits.
This approach towards the detection and study of very shallow transits is bound
to play a significant role in future space-based transit surveys such as PLATO,
which are specifically aimed to detect those extremely difficult cases of
long-period shallow transits. Our segmentation network also adds to the growing
toolbox of deep learning approaches which are being increasingly used in the
study of exoplanets, but so far mainly for vetting transits, rather than their
initial detection.
