  New deep learning techniques present promising new analysis methods for
Imaging Atmospheric Cherenkov Telescopes (IACTs) such as the upcoming Cherenkov
Telescope Array (CTA). In particular, the use of Convolutional Neural Networks
(CNNs) could provide a direct event classification method that uses the entire
information contained within the Cherenkov shower image, bypassing the need to
Hillas parameterise the image and allowing fast processing of the data.
  Existing work in this field has utilised images of the integrated charge from
IACT camera photomultipliers, however the majority of current and upcoming
generation IACT cameras have the capacity to read out the entire photosensor
waveform following a trigger. As the arrival times of Cherenkov photons from
Extensive Air Showers (EAS) at the camera plane are dependent upon the altitude
of their emission and the impact distance from the telescope, these waveforms
contain information potentially useful for IACT event classification.
  In this test-of-concept simulation study, we investigate the potential for
using these camera pixel waveforms with new deep learning techniques as a
background rejection method, against both proton and electron induced EAS. We
find that a means of utilising their information is to create a set of seven
additional 2-dimensional pixel maps of waveform parameters, to be fed into the
machine learning algorithm along with the integrated charge image. Whilst we
ultimately find that the only classification power against electrons is based
upon event direction, methods based upon timing information appear to
out-perform similar charge based methods for gamma/hadron separation. We also
review existing methods of event classifications using a combination of deep
learning and timing information in other astroparticle physics experiments.
