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The Cherenkov Telescope Array (CTA) will be the next generation gamma-rayobservatory and will be the major global instrument for very-high-energyastronomy over the next decade, offering 5 - 10 x better flux sensitivity thancurrent generation gamma-ray telescopes. Each telescope will provide a snapshotof gamma-ray induced particle showers by capturing the induced Cherenkovemission at ground level. The simulation of such events provides images thatcan be used as training data for convolutional neural networks (CNNs) todetermine the energy of the initial gamma rays. Compared to otherstate-of-the-art algorithms, analyses based on CNNs promise to further enhancethe performance to be achieved by CTA. Pattern spectra are commonly used toolsfor image classification and provide the distributions of the shapes and sizesof various objects comprising an image. The use of relatively shallow CNNs onpattern spectra would automatically select relevant combinations of featureswithin an image, taking advantage of the 2D nature of pattern spectra. In thiswork, we generate pattern spectra from simulated gamma-ray events instead ofusing the raw images themselves in order to train our CNN for energyreconstruction. This is different from other relevant learning and featureselection methods that have been tried in the past. Thereby, we aim to obtain asignificantly faster and less computationally intensive algorithm, with minimalloss of performance.

Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array data