Toolkit to interface and run machine learning methods together with the Eventdisplay software package for gamma-ray astronomy data analysis.
Provides examples on how to use e.g., scikit-learn or XGBoost regression trees to estimate event direction, energies, and gamma/hadron separators.
Introduces a Python environment and a scripts directory to support training and inference.
Stereo analysis methods implemented in Eventdisplay provide direction / energies per event resp telescope image. The machine learner implemented Eventdisplay-ML uses XGB Boost regression trees. Features are all estimators (e.g. DispBDT or intersection method results) plus additional features (mostly image parameters) to get a better estimator for directions and energies.
Input is provided through the mscw output (data trees).
Output is a single ROOT tree called StereAnalysis with the same number of events as the input tree.
Please cite this software if it is us ed for a publication, see the Zenodo record and CITATION.cff for details.