This repository is forked from this Github, which is the official repository for the code and pretrained models of "A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer". The Jupyter notebook LabelingWithoutMatching.ipynb shows how the regression tools from the CPT paper can be re-used for jet labeling and includes the plots for the paper "Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models". All the other instructions below are unchanged from the CPT GitHub.
Follow these instructions to set up the environment, download pretrained models, and download datasets used in the paper:
- Set up conda environment and install dependencies:
sh setup.sh
conda activate cpt
-
(Optional) Download pretrained
$ttH$ and$tttt$ models:
The script also downloads model predictions on all datasets used in the paper.
sh download_pretrained.sh
-
(Optional) Download datasets:
Note the
$ttH$ and$tttt$ dataset is ~30GB in size each, while the other datasets are smaller. You can comment out the lines indownload_datasets.shto download only the datasets you need.
sh download_datasets.sh
The current implementation of our datasets are not well-optimized for speed or size. We encourage making your own datasets and modifying the data processing code in dataset.py and utils.py to suit your needs.
To use the pretrained models, refer to the Jupyter notebooks pretrained_ttH.ipynb or pretrained_tttt.ipynb.
To train a new model, use the train.ipynb Jupyter notebook.
Please cite the CPT paper as:
@article{qiu2022holistic,
title={A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer},
author={Qiu, Shikai and Han, Shuo and Ju, Xiangyang and Nachman, Benjamin and Wang, Haichen},
journal={arXiv preprint arXiv:2203.05687},
year={2022}
}
and the labeling paper as
@article{qiu2023holistic,
title={Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models},
author={Qiu, Shikai and Han, Shuo and Ju, Xiangyang and Nachman, Benjamin and Wang, Haichen},
journal={arXiv preprint arXiv:2304.xxxxx},
year={2023}
}