gnn-jet-autoencoder
Graph neural network autoencoders for jets in HEP
Science Score: 54.0%
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Low similarity (7.2%) to scientific vocabulary
Keywords
Repository
Graph neural network autoencoders for jets in HEP
Basic Info
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- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 2
Topics
Metadata Files
README.md
Graph Neural Network Autoencoders Autoencoder for Jets
Overview
A graph autoencoder (GNNAE) for jets in particle physics implemented in PyTorch, mainly used as a baseline for LGAE
Data
To download data:
1. Install JetNet:
pip3 install jetnet;
2. Run preprocess.py
python utils/data/preprocess.py \
--jet-types g q t w z \
--save-dir "./data"
Training
To train the model, run train.py. An example is provided in examples/train.sh.
Architecture
Both the encoder and decoder are built upon the GraphNet architecture implemented in models/graphnet.py, which is a fully connected massage passing neural network.
The message passing step of GraphNet is shown in the diagram below. Here, $d$ is any distance function, and EdgeNet and NodeNet are edge and node functions at the $t$-th message passing step, respectively, both of which are MLPs with LeakyReLU activation.
Owner
- Name: Zichun Hao
- Login: zichunhao
- Kind: user
- Company: University of California, San Diego
- Repositories: 8
- Profile: https://github.com/zichunhao
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Hao
given-names: Zichun
orcid: https://orcid.org/0000-0002-5624-4907
- family-names: Kansal
given-names: Raghav
orcid: https://orcid.org/0000-0003-2445-1060
- family-names: Duarte
given-names: Javier
orcid: https://orcid.org/0000-0002-5076-7096
- family-names: Chernyavskaya
given-names: Nadezda
orcid: https://orcid.org/0000-0002-2264-2229
title: "Graph Neural Network Autoencoders for Jets"
version: 1.0.0
doi: 10.5281/zenodo.7453764
date-released: 2022-12-17
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- zichunhao (1)