A community-curated field guide to machine learning for particle physics, maintained by the HEP–ML community.
Live site: https://iml-wg.github.io/HEPML-LivingGuide/
This resource replaces the HEP–ML Living Review, which is now frozen as an archival bibliography. The Guide curates rather than enumerates: it offers structured entry points into the literature, annotated recommendations of foundational works, and community-driven guidance for researchers navigating a now mature and rapidly diversifying field. See the About page for the full rationale.
See CONTRIBUTING.md for the short version, or the Contribute page on the site for full guidance.
- Content (everything in
docs/) is licensed under CC BY 4.0. - Code (configuration, workflows, scripts) is licensed under the MIT License.
See LICENSE for the full text.
- Claudius Krause — Marietta Blau Institute for Particle Physics
- Ramon Winterhalder — Università degli Studi di Milano & INFN
- Matthew Feickert — University of Wisconsin–Madison
- Benjamin Nachman — SLAC & Stanford
See the Cite us page.