---

This is where we really bauen the Baums

This is the code for the paper Learning tree structures from leaves for particle decay reconstruction.

The dataset used in the study is available on Zenodo.

Installation

In order to set up the necessary environment:

1. Create a Python virtual environment for the project:

   python3 -m venv /path/to/baum-env
   

2. Activate the new environment with

   source /path/to/baum-env/bin/activate
   

3. The first time you'll need to update pip:

   pip3 install -U pip
   

4. Install the baumbauen package with:

   cd /path/to/baumbauen/repo
   pip install .
   

   or if you're doing development work and want code changes to be reflected in the installed package immediately:

   pip install -e .
   

5. (Optional) If you'd like to run the package tests then install pytest:

     pip install pytest pytest-cov
   

Usage

There are two ways to see usage of the package: the examples in the scripts and notebooks folders.

The scripts are for executing in a grid/batch environment, this includes basf2 steering files (requires Belle II software) as well as PyTorch training scripts.

The notebooks are self-contained and are ordered according to workflow by their numbering (lettering indicates author). They do require Jupytext to be able to read/commit notebooks (installed as a requirement for BaumBauen). When opening notebooks select File > Jupytext > Pair Notebook with light Script

Project Organization

├── AUTHORS.rst             <- List of developers and maintainers.
├── CHANGELOG.rst           <- Changelog to keep track of new features and fixes.
├── LICENSE                 <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── configs                 <- Directory for configurations of model & application.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── scripts                 <- Analysis and production scripts which import the
│                              actual PYTHON_PKG, e.g. train_model.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- Use `python setup.py develop` to install for development or
|                              or create a distribution with `python setup.py bdist_wheel`.
├── src
│   └── baumbauen           <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `py.test`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.

License

This project is release under the BSD-3-Clause license

Acknowledgement

This work is supported by the Helmholtz Association Initiative and Networking Fund under the Helmholtz AI platform grant and the HAICORE@KIT partition, the Bundesministerium für Bildung und Forschung (BMBF) under the grant 05H21PDKBA, and the L’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3) du CNRS (France) and the French Agence Nationale de la Recherche (ANR) under grant ANR-21-CE31-0009 (project FIDDLE) and the Seed Money programme of Eucor - The European Campus. We wish to also thank Julián Garcı́a Pardiñas and Isabelle Ripp-Baudot for the fruitful discussions.

Citation

If you use this work in your research, please cite this project.

@article{kahnLearningTreeStructures2022a,
    title = {Learning Tree Structures from Leaves for Particle Decay Reconstruction},
    author = {Kahn, James and Tsaklidis, Ilias and Taubert, Oskar and Reuter, Lea and Dujany, Giulio and Boeckh, Tobias and Thaller, Arthur and Goldenzweig, Pablo and Bernlochner, Florian and Streit, Achim and G{\"o}tz, Markus},
    year = {2022},
    month = sep,
    journal = {Machine Learning: Science and Technology},
    volume = {3},
    number = {3},
    pages = {035012},
    publisher = {{IOP Publishing}},
    issn = {2632-2153},
    doi = {10.1088/2632-2153/ac8de0},
    langid = {english}
}

Note

This project has been set up using PyScaffold 3.2.3 and the dsproject extension 0.4. For details and usage information on PyScaffold see https://pyscaffold.org/.