Source code for "PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction"
We propose a novel supervised graph contrastive learning with perturbation augmentation, which utilizes graph neural network to extract semantic features from the momentum and energy of particles for the particle decay reconstruction task.
The code structure is based in part on BaumBauen. (Thanks for their awesome work.)
The dataset is available on Zenodo.
conda create -yn pascl python=3.7
conda activate pascl
#update pip
pip3 install -U pip
git clone https://github.com/lukSYSU/PASCL.git
cd PASCL
pip install .
To train the PASCL, run the script scripts/training/train_model.py as follows:
cd scripts/training
python train_model.py -c config_yaml_name
Note that you should modify your own config_yaml_name file as input.