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Artificial Intelligence for Imaging Atmospheric Cherenkov Telescopes

Project description

aict-tools Build Status DOI PyPI version

Executables to perform machine learning tasks on FACT and CTA eventlist data. Possibly also able to handle input of other experiments if in the same file format.

All you ever wanted to do with your IACT data in one package. This project is mainly targeted at using machine-learning for the following tasks:

  • Energy Regression
  • Gamma/Hadron Separation
  • Reconstruction of origin (Mono for now)


If you use the aict-tools, please cite us like this using the doi provided by zenodo, e.g. like this if using bibtex files:

      author = {Nöthe, Maximilian and Brügge, Kai Arno and Buß, Jens Björn},
      title = {aict-tools},
      subtitle = {Reproducible Artificial Intelligence for Cherenkov Telescopes},
      doi = {10.5281/zenodo.3338081},
      url = {},


Then you can install the aict-tools by:

pip install aict-tools

By default, this does not install optional dependencies for writing out models in onnx or pmml format. If you want to serialize models to these formats, install this using:

$ pip install aict-tools[pmml] # for pmml support
$ pip install aict-tools[onnx] # for onnx support

In the case of working with CTA data, you will also need to have ctapipe installed. If this is not already the case, you can install it using:

$ pip install aict-tools[cta] # for DISP use on CTA data

To install all optional dependencies, use:

$ pip install aict-tools[all]  # for all

Alternatively you can clone the repo, cd into the folder and do the usual pip install . dance.


For each task, there are two executables, installed to your PATH. Each take yaml configuration files and h5py style hdf5 files as input. The models are saved as pickle using joblib and/or pmml using sklearn2pmml.

  • aict_train_<...>
    This script is used to train a model on events with known truth values for the target variable, usually monte carlo simulations.

  • aict_apply_<...> This script applies a given model, previously trained with aict_train_<...> and applies it to data, either a test data set or data with unknown truth values for the target variable.

The apply scripts can iterate through the data files in chunks using the --chunksize=<N> option, this can be handy for very large files (> 1 million events).

Energy Regression

Energy regression for gamma-rays require a yaml configuration file and simulated gamma-rays in the event list format.

The two scripts to perform energy regression are called

  • aict_train_energy_regressor
  • aict_apply_energy_regressor

An example configuration can be found in examples/config_energy.yaml.

To apply a model, use aict_apply_energy_regressor.


Binary classification or Separation requires a yaml configuration file, one data file for the signal class and one data file for the background class.

The two scripts to perform separation are called

  • aict_train_separation_model
  • aict_apply_separation_model.

An example configuration can be found in examples/config_separator.yaml.

Reconstruction of gamma-ray origin using the disp method

To estimate the origin of the gamma-rays in camera coordinates, the disp-method can be used.

Here it is implemented as a two step regression/classification task. One regression model is trained to estimate abs(disp) and a classification model is trained to estimate sgn(disp).

Training requires simulated diffuse gamma-ray events.

  • aict_train_disp_regressor
  • aict_apply_disp_regressor

An example configuration can be found in examples/config_source.yaml. Currently supported experiments:

  • FACT
  • CTA

Note: By applying the disp regressor, Theta wil be deleted from the feature set. Theta has to be calculated from the source prediction e.g. by using fact_calculate_theta from pyfact.

Utility scripts

Applying straight cuts

For data selection, e.g. to get rid of not well reconstructable events, it is customary to apply so called pre- or quality cuts before applying machine learning models.

This can be done with aict_apply_cuts and a yaml configuration file of the cuts to apply. See examples/quality_cuts.yaml for an example configuration file.

Split data into training/test sets

Using aict_split_data, a dataset can be randomly split into sets, e.g. to split a monte carlo simulation dataset into train and test set.

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