  Imaging atmospheric Cherenkov telescope (IACT) arrays record images from air
showers initiated by gamma rays entering the atmosphere, allowing astrophysical
sources to be observed at very high energies. To maximize IACT sensitivity,
gamma-ray showers must be efficiently distinguished from the dominant
background of cosmic-ray showers using images from multiple telescopes. A
combination of convolutional neural networks (CNNs) with a recurrent neural
network (RNN) has been proposed to perform this task. Using CTLearn, an open
source Python package using deep learning to analyze data from IACTs, with
simulated data from the upcoming Cherenkov Telescope Array (CTA), we implement
a CNN-RNN network and find no evidence that sorting telescope images by total
amplitude improves background rejection performance.
