  Machine learning, through the use of convolutional and recurrent neural
networks is a promising avenue for the improvement of background rejection
performance in imaging atmospheric Cherenkov telescopes. However, it is of
paramount importance for science analysis that their performance remains stable
against a wide range of observing conditions and instrument states.
  We investigate the stability of convolutional recurrent networks by applying
them to background rejection in a toy Monte Carlo simulation of a Cherenkov
telescope array. We then vary a range of observation and instrument parameters
in the simulation. In general, most of the resulting systematics are at a level
not much greater than conventional analyses. However, a strong dependence of
the neural network predictions on the noise level within the camera was found,
with differences of up to 50% in the gamma-ray acceptance rate in very noisy
environments. It is clear from the performance differences seen in these
studies that these observational effects must be considered in the training
step of the final analysis when using such networks for background rejection in
Cherenkov telescope observations.
