  We describe a method of reconstructing air showers induced by cosmic rays
using deep learning techniques. We simulate an observatory consisting of
ground-based particle detectors with fixed locations on a regular grid. The
detector's responses to traversing shower particles are signal amplitudes as a
function of time, which provide information on transverse and longitudinal
shower properties. In order to take advantage of convolutional network
techniques specialized in local pattern recognition, we convert all information
to the image-like grid of the detectors. In this way, multiple features, such
as arrival times of the first particles and optimized characterizations of time
traces, are processed by the network. The reconstruction quality of the cosmic
ray arrival direction turns out to be competitive with an analytic
reconstruction algorithm. The reconstructed shower direction, energy and shower
depth show the expected improvement in resolution for higher cosmic ray energy.
