All software
2024_domain_adaptation_methods_lst_results
Results from the study presented in the article entilted _Comparative study of unsupervised domain adaptation techniques applied to gamma-ray astronomy with the CTAO Large Sized Telescope-1_
- Astroparticle Physics
- CTA
- Deep learning
- + 1
2025-stereograph
This repository contains pre-trained models, computed results, and analysis code for evaluating machine learning approaches (Random Forests, FCNs, and GNNs) on gamma-ray event reconstruction tasks.
- Astroparticle Physics
- CTA
- Deep learning
- + 2
- ("Jupyter Notebook")
- (Shell)
CTLearn: Deep learning for imaging atmospheric Cherenkov telescopes event reconstruction
CTLearn is a high-level Python package providing a backend for training deep learning models for the reconstruction of imaging atmospheric Cherenkov telescope events using TensorFlow.
- Deep learning
- Event reconstruction
- High energy physics
- + 1
- (Dockerfile)
- ("Jupyter Notebook")
- (Python)
GammaLearn
GammaLearn is a collaborative project to apply deep learning to the analysis of low-level Imaging Atmospheric Cherenkov Telescopes such as CTA. It provides a framework to easily train and apply models from a configuration file. Learn more at https://purl.org/gammalearn
- CTA
- Deep learning
- Gamma-ray telescopes
- + 1
- (Dockerfile)
- (Python)