CTLearn: Deep Learning for IACT Event Reconstruction

CTLearn is a package under active development to run deep learning models to analyze data from all major current and future arrays of imaging atmospheric Cherenkov telescopes (IACTs). CTLearn can load R1/DL0/DL1 data from CTAO (Cherenkov Telescope Array Observatory), FACT, H.E.S.S., LST-1, MAGIC, and VERITAS telescopes reduced by ctapipe and processed by DL1DataHandler.

• Code, feature requests, bug reports, pull requests: https://github.com/ctlearn-project/ctlearn
• Documentation: https://ctlearn.readthedocs.io
• License: BSD-3

Installation for users

Installation

First, create and activate a fresh conda environment:

mamba create -n ctlearn -c conda-forge python==3.12 llvmlite
conda activate ctlearn

The lastest version fo this package can be installed as a pip package:

pip install ctlearn

See the documentation for further information like installation instructions for the IT-cluster, installation instructions for developers, package usage, and dependencies among other topics.

Citing this software

Please cite the corresponding version using the DOIs from Zenodo if this software package is used to produce results for any publication.

Team

Ari Brill	Bryan Kim	Tjark Miener	Daniel Nieto

Collaborators

Qi Feng	Ruben Lopez-Coto

Alumni

Jaime Sevilla	Héctor Rueda	Juan Redondo Pizarro	Luca Romanato	Sahil Yadav	Sergio García Heredia