  Recent developments in machine learning (ML) techniques present a promising
new analysis method for high-speed imaging in astroparticle physics
experiments, for example with imaging atmospheric Cherenkov telescopes (IACTs).
In particular, the use of timing information with new machine learning
techniques provides a novel method for event classification. Previous work in
this field has utilised images of the integrated charge from IACT camera
photomultipliers, but the majority of current and upcoming 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, these
waveforms contain information useful for IACT event classification. In this
work, we investigate the potential for using these waveforms with ML
techniques, and find that a highly effective means of utilising their
information is to create a set of seven additional two dimensional histograms
of waveform parameters to be fed into the machine learning algorithm along with
the integrated charge image. This appears to be superior to using only these
new ML techniques with the waveform integrated charge alone. We also examine
these timing-based ML techniques in the context of other experiments.
