  At GeV energies, the sky is dominated by the interstellar emission from the
Galaxy. With limited statistics and spatial resolution, accurate separation of
point sources is therefore challenging. Here we present the first application
of deep learning based algorithms to automatically detect and classify point
sources from gamma-ray data. To detect point sources we utilise U-shaped
convolutional networks for image segmentation and k-means for source clustering
and localisation. We also explore the Centroid-Net algorithm, which is designed
to find and count objects. Using two algorithms allows for a cross check of the
results, while combination of their results can be used to improve performance.
The training data is based on 9.5 years of Fermi-LAT exposure and we use source
properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth
Fermi-LAT source catalog (4FGL) in addition to several models of background
interstellar emission.
  We compare our algorithms qualitatively with traditional methods and find
them to have similar detection thresholds. We also demonstrate the robustness
of our source localization algorithms to modifications in the interstellar
emission models, which presents a clear advantage over traditional methods. The
results of the localization algorithm are fed into a classification neural
network that is trained to separate the three general source classes (AGNs,
PSRs, and FAKE sources). The network is able to discriminate between these
three classes with global accuracy of $\sim$ 70%, as long as balanced data sets
are used in classification training. In
https://github.com/bapanes/Gamma-Ray-Point-Source-Detector.git we publish our
training data sets and analysis scripts and invite the community to the data
challenge: How can we best locate and classify gamma-ray point sources?
