Multi-View Deep Learning for Imaging Atmospheric Cherenkov Telescopes

Abstract

This research note concerns the application of deep-learning-based multi-view-imaging techniques to data from the H.E.S.S. Imaging Atmospheric Cherenkov Telescope array. We find that the earlier the fusion of layer information from different views takes place in the neural network, the better our model performs with this data. Our analysis shows that the point in the network where the information from the different views is combined is far more important for the model performance than the method used to combine the information.

arXiv commentAccepted in Research Notes of the American Astronomical Society. 3 Pages, 1 Figure

References in corpus (5)

• Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data
• Extracting gamma-ray information from images with convolutional neural network methods on simulated Cherenkov Telescope Array data
• Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array
• Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes
• Application of Graph Networks to background rejection in Imaging Air Cherenkov Telescopes