Multi-View Deep Learning for Imaging Atmospheric Cherenkov Telescopes
Research Notes of the AAS(2024)
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.
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