Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Living Review of Machine Learning for Particle Physics
| Date | Stars |
|---|---|
| 2026-07-31 | 444 |
| 2026-08-06 | 445 |
| 2026-08-11 | 447 |
| 2026-08-13 | 448 |
| 2026-08-14 | 448 |
| 2026-08-15 | 448 |
| 2026-08-18 | 448 |
| 2026-08-23 | 448 |
| 2026-08-27 | 447 |
| 2026-08-31 | 448 |
| 2026-09-09 | 449 |
| 2026-09-18 | 451 |
| 2026-09-23 | 452 |
| 2026-09-24 | 453 |
| 2026-09-29 | 453 |
| 2026-10-11 | 453 |
| 2026-10-27 | 453 |
| 2026-11-04 | 453 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
5.0
growth rate 0.00%/day
# **A Living Review of Machine Learning for Particle Physics**
> [!IMPORTANT]
> This project is no longer actively maintained. The literature coverage is
> frozen as of 1 June 2026. The final archival version is available on
> [Zenodo](https://doi.org/10.5281/zenodo.21626667).
*Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. The goal of this document is to provide a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. A list of conventional, unchanging reviews can be found within. Papers are grouped into a small set of topics to be as useful as possible.*
[](https://iml-wg.github.io/HEPML-LivingReview/assets/hepml_review.pdf)
[](https://github.com/iml-wg/HEPML-LivingReview)
<p align="center"><img src="docs/assets/per_year.png" width="75%", alt="Publications per Year"></p>
The purpose of this note is to collect references for modern machine learning as applied to particle physics. A minimal number of categories is chosen in order to be as useful as possible. Note that papers may be referenced in more than one category. The fact that a paper is listed in this document does not endorse or validate its content—that is for the community and peer review to decide. The classification represents our best attempt at the time the review was frozen, with literature coverage through 1 June 2026.
If you find this review helpful, please cite the final archived version using ```\cite{krause_2026_21626667}``` in `HEPML.bib`.
This review was built with the help of the HEP-ML community, the [INSPIRE REST API](https://github.com/inspirehep/rest-api-doc), and the moderators Benjamin Nachman, Matthew Feickert, Claudius Krause, John Andrew Raine, and Ramon Winterhalder.
## Reviews
### Modern reviews
* [Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision](https://arxiv.org/abs/2602.17582) (2026)
* [Machine Learning](https://arxiv.org/abs/2512.11133) (2025)
* [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://arxiv.org/abs/2209.07559) (2022)
* [Artificial Intelligence and Machine Learning in Nuclear Physics](https://arxiv.org/abs/2112.02309) [[DOI](https://doi.org/10.1103/RevModPhys.94.031003)] (2021)
* [Machine Learning in the Search for New Fundamental Physics](https://arxiv.org/abs/2112.03769) (2021)
* [Modern Machine Learning and Particle Physics](https://arxiv.org/abs/2103.12226) [[DOI](https://doi.org/10.1162/99608f92.beeb1183)] (2021)
* [Machine and Deep Learning Applications in Particle Physics](https://arxiv.org/abs/1912.08245) [[DOI](https://doi.org/10.1142/S0217751X19300199)] (2019)
* [Machine learning and the physical sciences](https://arxiv.org/abs/1903.10563) [[DOI](https://doi.org/10.1103/RevModPhys.91.045002)] (2019)
* [Machine learning at the energy and intensity frontiers of particle physics](https://doi.org/10.1038/s41586-018-0361-2) (2018)
* [Machine Learning in High Energy Physics Community White Paper](https://arxiv.org/abs/1807.02876) [[DOI](https://doi.org/10.1088/1742-6596/1085/2/022008)] (2018)
* [Deep Learning and its Application to LHC Physics](https://arxiv.org/abs/1806.11484) [[DOI](https://doi.org/10.1146/annurev-nucl-101917-021019)] (2018)
* [Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning](https://arxiv.org/abs/1709.04464) [[DOI](https://doi.org/10.1016/j.physrep.2019.11.001)] (2017)
### Specialized reviews
* [Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice](https://arxiv.org/abs/2605.31103) (2026)
* [Toward a Community Roadmap for High Energy Physics and Artificial Intelligence in China and Beyond](https://arxiv.oExcerpt of 511,590 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9ed2b24a5258b44c, topic:papers, readme:a list of, readme:roadmap