clic/clusters/v1.6/README.md

Model Card for mlpf-clic-clusters-v1.6

This model reconstructs particles in a detector, based on the tracks and calorimeter clusters recorded by the detector.

Model Details

Model Description

• Developed by: Joosep Pata, Eric Wulff, Farouk Mokhtar, Mengke Zhang, David Southwick, Maria Girone, David Southwick, Javier Duarte
• Model type: graph neural network with learnable structure in locality-sensitive hashing bins
• License: Apache License

Model Sources

• Repository: https://github.com/jpata/particleflow/releases/tag/v1.6
• Paper: https://doi.org/10.48550/arXiv.2309.06782

Uses

Direct Use

This model may be used to study the physics and computational performance on ML-based reconstruction in simulation.

Out-of-Scope Use

This model is not intended for physics measurements on real data.

Bias, Risks, and Limitations

The model has only been trained on simulation data and has not been validated against real data.

How to Get Started with the Model

Use the code below to get started with the model.

git clone https://github.com/jpata/particleflow/releases/tag/v1.6
cd particleflow

#Download the software image
wget https://hep.kbfi.ee/~joosep/tf-2.14.0.simg

#Download the checkpoint
wget https://huggingface.co/jpata/particleflow/resolve/clic_clusters_v1.6/weights-96-5.346523.hdf5
wget https://huggingface.co/jpata/particleflow/resolve/clic_clusters_v1.6/opt-96-5.346523.pkl

#Launch a shell in the software image
apptainer shell --nv tf-2.14.0.simg

#Continue the training from a checkpoint
python3 mlpf/pipeline.py train --config parameters/clic.yaml --weights weights-96-5.346523.hdf5 --batch-multiplier 0.5

#Run the evaluation for a given training directory, loading the best weight file in the directory
python3 mlpf/pipeline.py evaluate --train-dir experiments/clic-REPLACEME

Training Details

Training Data

Trained on the following dataset: Pata, J., Wulff, E., Duarte, J., Mokhtar, F., Zhang, M., Girone, M., & Southwick, D. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format (v1.5.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8409592

Training Procedure

python3 mlpf/pipeline.py train --config parameters/clic.yaml

Evaluation

python3 mlpf/pipeline.py evaluate --train-dir experiments/clic-REPLACEME

Citation

BibTeX:

@misc{pata2023scalable,
      title={Scalable neural network models and terascale datasets for particle-flow reconstruction}, 
      author={Joosep Pata and Eric Wulff and Farouk Mokhtar and David Southwick and Mengke Zhang and Maria Girone and Javier Duarte},
      year={2023},
      eprint={2309.06782},
      archivePrefix={arXiv},
      primaryClass={physics.data-an}
}

Glossary

PF - particle flow reconstruction

Model Card Contact

Joosep Pata, joosep.pata@cern.ch