Main Image
Volume 358 - 36th International Cosmic Ray Conference (ICRC2019) - GRI - Gamma Ray Indirect
CTLearn: Deep Learning for Gamma-ray Astronomy
D. Nieto Castaño,* A. Brill, Q. Feng, T.B. Humensky, B. Kim, T. Miener, R. Mukherjee, J. Sevilla
*corresponding author
Full text: pdf
Pre-published on: 2019 July 22
Published on:
Abstract
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 loading and manipulating IACT data and 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.
Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.