Related living resources¶
The HEP–ML Living Guide is one of several community-curated resources that have emerged in neighboring areas, all built on the principle that orientation in a fast-moving field deserves its own dedicated infrastructure.
Their existence is what lets this Guide keep a narrow scope. The areas below are deliberately not covered here — they are covered better by the people who work in them.
Outside our scope — go here instead¶
- Machine Learning in Cosmology, George Stein — https://github.com/georgestein/ml-in-cosmology — cosmology and large-scale structure.
- Awesome Astrodata, Michael Gully-Santiago — https://github.com/gully/awesome-astrodata — astronomical data science.
- The AI/ML for Particle Accelerators Living Review — https://aghribi.github.io/acc-ml-living-review/ — accelerator design, operation, and controls.
Neighboring HEP resources¶
- Awesome HEP, IRIS-HEP — https://github.com/iris-hep/awesome-hep — software-focused field guide to HEP, complementary to this one.
Method-specific resources¶
- Simulation-based inference, Kyle Cranmer and Johann Lo — https://simulation-based-inference.org — cross-disciplinary guide to SBI, with HEP applications prominently featured.
- Awesome Neural SBI, Siddharth Mishra-Sharma — https://github.com/smsharma/awesome-neural-sbi
The predecessor¶
- The HEP–ML Living Review (archived) — https://iml-wg.github.io/HEPML-LivingReview/, archived at Zenodo — the bibliographic record of HEP–ML literature until 1 June 2026. See The archived Living Review for context.