CTLearn: Deep Learning for Gamma-ray Astronomy

TL;DR

CTLearn is a Python package that applies deep learning techniques to analyze imaging atmospheric Cherenkov telescope data, improving gamma-ray source detection by efficiently rejecting cosmic-ray background.

Contribution

It introduces a new Python toolkit integrating TensorFlow for deep learning analysis of IACT data, enabling reproducible workflows and improved background rejection.

Findings

Demonstrated effective background rejection on simulated data

Showcased deep learning model training and prediction workflows

Enhanced gamma-ray source detection capabilities

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.