HEP–ML Community
HEP–ML Living Guide¶
A community-curated field guide to machine learning for particle physics — opinionated entry points, annotated reading lists, and a structured way into a fast-moving literature.
This resource replaces the HEP–ML Living Review
The original Living Review of Machine Learning for Particle Physics is frozen as an archival reference covering the literature until 1 June 2026. The Living Guide is its successor: a curated, annotated entry point into a now mature and diversifying field. Read more on the About page.
What this guide is¶
Machine learning in particle physics has grown to over 4 000 papers and several hundred new ones each year. A comprehensive list is no longer the most useful resource a researcher needs — INSPIRE-HEP and arXiv already do that job well. This guide answers a different question:
Where should I start, what matters, and why?
Each section is written by a researcher active in the area. Every recommendation comes with a sentence of context. The guide is opinionated by design — it is not a search index, it is a map.
How to navigate¶
You can enter the literature along two independent axes — pick whichever matches how you're thinking about a problem.
-
By HEP application
Start here if you have a physics problem in mind — fast simulation, anomaly detection in collider data, unfolding, triggering, etc.
-
By ML method
Start here if you're curious about a methodology — diffusion models, equivariant networks, simulation-based inference, foundation models.
Quick links¶
- How to use this guide — what each section contains, and what it deliberately leaves out
- Reviews & lecture notes — the best places to read more deeply
- Benchmarks & community challenges — shared datasets and competitions
- Related living resources — guides covering adjacent fields that this one deliberately leaves out
- Contribute — write a section, suggest a paper, flag an error
Citing the Guide¶
If you find the guide useful in your work, please cite it. See Cite us for the recommended BibTeX entry.