QUAK
QUasi Anomalous Knowledge for Anomaly Detection and Tagging in High Energy Physics
Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
This repository is the official implementation of Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge.
Requirements
I used conda to manage my dependencies.
To install requirements:
conda env create -f environment.yml
Datasets
QUAK used LHC Olympics dataset curated by Kasieczka, Gregor; Nachman, Benjamin; Shih, David, please cite https://zenodo.org/record/4536624 To read more about these datasets, please read LHC Olympics Community White Paper: https://arxiv.org/abs/2101.08320
We privately generated samples based on LHC Olympics dataset, the procedure of which is outlined in our paper: https://arxiv.org/abs/2011.03550 For training and evaluation, we applied pre-processing procedure which leaves each event with jet masses and substructure variables.
QUAK method is not limited to physics dataset; It can be applied to any environment where having vague knowledge of anomaly could help with the detection. To test QUAK in a different setting, we tested it on MNIST dataset (http://yann.lecun.com/exdb/mnist/).
Training
To train the model(s) in the paper, run this command:
python train_script.py
Evaluation
To evaluate QUAK performance on LHC Olympics black box dataset, run:
python eval.py --model-file mymodel.pth --benchmark imagenet
To evaluate QUAK performance on MNIST dataset, run:
Citation
We are preparing a journal submission, in the meantime, please cite our paper from arxiv:
@article{Park:2020pak, author = "Park, Sang Eon and Rankin, Dylan and Udrescu, Silviu-Marian and Yunus, Mikaeel and Harris, Philip", title = "{Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge}", eprint = "2011.03550", archivePrefix = "arXiv", primaryClass = "hep-ph", month = "11", year = "2020" }
Pre-trained Models
You can download pretrained models here:
- My awesome model trained on ImageNet using parameters x,y,z.
- My awesome model trained on ImageNet using parameters x,y,z.
Results
Our model achieves the following performance on :
Image Classification on ImageNet
| Model name | Top 1 Accuracy | Top 5 Accuracy |
|---|---|---|
| My awesome model | 85% | 95% |