  The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray
observatory and will be the major global instrument for very-high-energy
astronomy over the next decade, offering 5 - 10 x better flux sensitivity than
current generation gamma-ray telescopes. Each telescope will provide a snapshot
of gamma-ray induced particle showers by capturing the induced Cherenkov
emission at ground level. The simulation of such events provides images that
can be used as training data for convolutional neural networks (CNNs) to
determine the energy of the initial gamma rays. Compared to other
state-of-the-art algorithms, analyses based on CNNs promise to further enhance
the performance to be achieved by CTA. Pattern spectra are commonly used tools
for image classification and provide the distributions of the shapes and sizes
of various objects comprising an image. The use of relatively shallow CNNs on
pattern spectra would automatically select relevant combinations of features
within an image, taking advantage of the 2D nature of pattern spectra. In this
work, we generate pattern spectra from simulated gamma-ray events instead of
using the raw images themselves in order to train our CNN for energy
reconstruction. This is different from other relevant learning and feature
selection methods that have been tried in the past. Thereby, we aim to obtain a
significantly faster and less computationally intensive algorithm, with minimal
loss of performance.
