Speaker
Description
Imaging atmospheric Cherenkov telescope (IACT) arrays are designed to detect astrophysical sources at very high energies, through image analysis of air showers initiated by gamma rays entering the atmosphere. It is crucial for IACTs to separate the multi-telescope stereo images of gamma-ray signals from the background of charged cosmic-ray particles, the flux of which is several orders of magnitude greater. We use a combination of convolutional neural networks (CNNs) with a recurrent neural network (RNN) to achieve this task in CTLearn, an open source Python package using deep learning to analyze data from IACTs. To allow convolutions of images recorded by some telescopes with a hexagonal pixel layout, we experiment with multiple methods (e.g., oversampling or interpolation). In this workshop, we present the initial performance of the CNN-RNN models and the several methods to process images with hexagonal pixels implemented in CTLearn.
