  The Cosmic Multiperspective Event Tracker (CoMET) R&D project aims to
optimize the techniques for the detection of soft-spectrum sources through
very-high-energy gamma-ray observations using particle detectors (called ALTO
detectors), and atmospheric Cherenkov light collectors (called CLiC detectors).
The accurate reconstruction of the energies and maximum depths of gamma-ray
events using a surface array only, is an especially challenging problem at low
energies, and the focus of the project. In this contribution, we leverage
Convolutional Neural Networks (CNNs) using the ALTO detectors only, to try to
improve reconstruction performance at lower energies ( < 1 TeV ) as compared to
the SEMLA analysis procedure, which is a more traditional method using manually
derived features.
