  Continued improvements on existing reconstruction methods are vital to the
success of high-energy physics experiments, such as the IceCube Neutrino
Observatory. In IceCube, further challenges arise as the detector is situated
at the geographic South Pole where computational resources are limited.
However, to perform real-time analyses and to issue alerts to telescopes around
the world, powerful and fast reconstruction methods are desired. Deep neural
networks can be extremely powerful, and their usage is computationally
inexpensive once the networks are trained. These characteristics make a deep
learning-based approach an excellent candidate for the application in IceCube.
A reconstruction method based on convolutional architectures and hexagonally
shaped kernels is presented. The presented method is robust towards systematic
uncertainties in the simulation and has been tested on experimental data. In
comparison to standard reconstruction methods in IceCube, it can improve upon
the reconstruction accuracy, while reducing the time necessary to run the
reconstruction by two to three orders of magnitude.
