ctlearn 0.7.0

Deep learning analysis framework for Imaging Atmospheric Cherenkov Telescopes, especially the Cherenkov Telescope Array (CTA) and the MAGIC telescopes.

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 DL1 data from CTA (Cherenkov Telescope Array), FACT, H.E.S.S., MAGIC, and VERITAS telescopes processed by ctapipe or 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

Download and install Anaconda, or, for a minimal installation, Miniconda.

The following command will set up a conda virtual environment, add the necessary package channels, and install CTLearn specified version and its dependencies:

CTLEARN_VER=0.7.0
wget https://raw.githubusercontent.com/ctlearn-project/ctlearn/v$CTLEARN_VER/environment.yml
conda env create -n [ENVIRONMENT_NAME] -f environment.yml
conda activate [ENVIRONMENT_NAME]
pip install ctlearn==$CTLEARN_VER
ctlearn -h

This should automatically install all dependencies (NOTE: this may take some time, as by default MKL is included as a dependency of NumPy and it is very large).

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

Citing this software

Please cite the corresponding version using the DOIs below if this software package is used to produce results for any publication:

• 0.6.0 :

• 0.5.2 :

• 0.5.1 :

• 0.5.0 :

• 0.4.0 :

• 0.4.0-legacy :

• 0.3.1 :

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