This experiment demonstrates the usage of the two class neural network module on the telescope data set.
In this experiment a two class neural network module is used to train models to recognize gamma (signal), hadron (background) in a ground-based atmospheric Cherenkov gamma telescope. Cherenkov gamma telescope observes high energy gamma rays, taking advantage of the radiation emitted by charged particles produced inside the electromagnetic showers initiated by the gammas, and developing in the atmosphere. This Cherenkov radiation (of visible to UV wavelengths) leaks through the atmosphere and gets recorded in the detector, allowing reconstruction of the shower parameters. The available information consists of pulses left by the incoming Cherenkov photons on the photomultiplier tubes, arranged in a plane, the camera. Depending on the energy of the primary gamma, a total of few hundreds to some 10000 Cherenkov photons get collected, in patterns (called the shower image), allowing to discriminate statistically those caused by primary gammas (signal) from the images of hadronic showers initiated by cosmic rays in the upper atmosphere (background). source --https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope.