Atmospheric Cherenkov Telescope

Imaging Atmospheric Cherenkov Telescopes (IACTs) detect high-energy gamma rays by imaging the Cherenkov light produced by extensive air showers. Current research focuses on improving gamma/hadron separation and energy reconstruction using machine learning techniques, particularly deep learning models like convolutional neural networks and generative adversarial networks (GANs), often incorporating Hillas parameters or directly processing image data. These advancements enhance the sensitivity and accuracy of gamma-ray astronomy, impacting astrophysical studies and potentially finding applications in medical imaging, such as precise radiotherapy patient positioning.