  Imaging atmospheric Cherenkov telescopes (IACTs) are sensitive to rare
gamma-ray photons, buried in the background of charged cosmic-ray (CR)
particles, the flux of which is several orders of magnitude greater. The
ability to separate gamma rays from CR particles is important, as it is
directly related to the sensitivity of the instrument. This
gamma-ray/CR-particle classification problem in IACT data analysis can be
treated with the rapidly-advancing machine learning algorithms, which have the
potential to outperform the traditional box-cut methods on image parameters. We
present preliminary results of a precise classification of a small set of muon
events using a convolutional neural networks model with the raw images as input
features. We also show the possibility of using the convolutional neural
networks model for regression problems, such as the radius and brightness
measurement of muon events, which can be used to calibrate the throughput
efficiency of IACTs.
