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Machine learning, through the use of convolutional and recurrent neuralnetworks is a promising avenue for the improvement of background rejectionperformance in imaging atmospheric Cherenkov telescopes. However, it is ofparamount importance for science analysis that their performance remains stableagainst a wide range of observing conditions and instrument states.  We investigate the stability of convolutional recurrent networks by applyingthem to background rejection in a toy Monte Carlo simulation of a Cherenkovtelescope array. We then vary a range of observation and instrument parametersin the simulation. In general, most of the resulting systematics are at a levelnot much greater than conventional analyses. However, a strong dependence ofthe 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 noisyenvironments. It is clear from the performance differences seen in thesestudies that these observational effects must be considered in the trainingstep of the final analysis when using such networks for background rejection inCherenkov telescope observations.

Investigations of the Systematic Uncertainties in Convolutional Neural Network Based Analysis of Atmospheric Cherenkov Telescope Data