  CTLearn is a new Python package under development that uses the deep learning
technique to analyze data from imaging atmospheric Cherenkov telescope (IACT)
arrays. IACTs use the Cherenkov light emitted from air showers, initiated by
very-high-energy gamma rays, to form an image of the longitudinal development
of the air shower on the camera plane. The spatial, temporal, and calorimetric
information of the originating high-energy particle is then recorded
electronically. The sensitivity of IACTs to astrophysical sources depends
strongly on the efficient rejection of the background of much more numerous
cosmic-ray showers. CTLearn includes modules for running machine learning
models with TensorFlow, using pixel-wise camera data as input. Its high-level
interface provides a configuration-file-based workflow to drive reproducible
training and prediction. We illustrate the capabilities of CTLearn by
presenting some results using IACT simulated data.
