Year: 2019
Type: preprint
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 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.
Source: arXiv (Cornell University)
Authors: Nieto, D., Brill, A., Feng, Q., Humensky, T. B., Kim, B., Miener, T., Mukherjee, R., Sevilla, J.
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
Awards: Particle Astrophysics with VERITAS and Development for CTA, Collaborative Research: MRI Consortium: Development of a Novel Telescope for Very High-Energy Gamma-Ray Astrophysics, Collaborative Research: MRI Consortium: Development of a Novel Telescope for Very High-Energy Gamma-Ray Astrophysics
OpenAlex ID: W2995004454