LHC New Physics Dataset
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Keywords
Citation
- Thea Aarrestad, Ekaterina Govorkova, Jennifer Ngadiuba, Ema Puljak, Maurizio Pierini, and Kinga Anna Wozniak. Unsupervised new physics detection at 40 mhz: training dataset. 2021. URL: https://zenodo.org/record/5046389, doi:10.5281/ZENODO.5046389.
@misc{https://doi.org/10.5281/zenodo.5046389,
author = {Aarrestad, Thea and Govorkova, Ekaterina and Ngadiuba, Jennifer and Puljak, Ema and Pierini, Maurizio and Wozniak, Kinga Anna},
copyright = {Creative Commons Attribution 4.0 International},
doi = {10.5281/ZENODO.5046389},
publisher = {Zenodo},
title = {Unsupervised New Physics detection at 40 MHz: Training Dataset},
url = {https://zenodo.org/record/5046389},
year = {2021}
}
Ratings
CategoryRating
Software
3.00
While not formally evaluated in the previous version, Zenodo and paper links suggest available code for baseline models
(e.g., autoencoders, GANs), though they are scattered and not unified in a single repository.
Specification
3.00
The task and context are clearly described, but system constraints and formal inputs/outputs are not fully specified.
Dataset
5.00
Large-scale dataset hosted on Zenodo, publicly available, well-documented, with defined train/test structure.
Appears to follow at least 4 FAIR principles.
Metrics
4.00
Uses reasonable metrics (ROC-AUC, detection efficiency) that capture performance but lacks
full explanation and standard evaluation tools.
Reference Solution
2.00
Baselines are described across multiple papers but lack centralized, reproducible implementations
and hardware/software setup details.
Documentation
3.00
Some description in papers and dataset metadata exists, but lacks a unified guide, README,
or training setup in a central location.
Average rating: 3.33/5
Radar plot
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