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About the HEP–ML Living Guide

The HEP–ML Living Guide is a community-curated field guide to machine learning in particle physics. It is maintained by the HEP–ML community: anyone working in the field can write, revise, or correct it.

It is the successor to the HEP–ML Living Review, which was introduced in 2020 as a community-maintained, near-comprehensive bibliography. As the field has matured and the literature has grown by more than an order of magnitude, the original model — exhaustive aggregation organized by topic — has become both unsustainable to maintain and less useful to researchers. The Guide replaces enumeration with curation.

What changed

The full reasoning is laid out in the transition paper (see Cite us). In brief:

  • Scale. The Living Review now lists over 4 000 papers, with several hundred added each year. Near-comprehensive manual curation at this rate is not compatible with a volunteer-maintained resource.
  • Breadth. Machine learning in HEP has spawned subfields with their own reviews, benchmarks, and workshops. A single flat list no longer reflects how the literature is organized in practice.
  • Maturity. Discoverability is well served by INSPIRE-HEP and arXiv, and by the search tools researchers already use daily. Finding papers is not the problem any more. The remaining problem is navigation: how to enter a subfield, identify foundational papers, and understand what is established versus what is open.

Scope

The Guide covers machine learning for particle physics, and states that boundary explicitly because curation requires one. A bibliography can absorb a vague boundary — an extra entry costs almost nothing. A curated guide cannot, because every decision about what to include is implicitly a decision about what the field is.

In scope. Collider physics and phenomenology, formal and theoretical particle physics, lattice field theory, and neutrino physics. Work belongs here when either the physics problem or the methodological development is specific to this domain.

Out of scope. Nuclear and heavy-ion physics, astroparticle physics, cosmology, and astronomical data science, machine learning for accelerator design and operation, and generic machine-learning methodology with no particle-physics-specific content.

This is a deliberate narrowing relative to the archived Living Review, whose title referred to particle and nuclear physics. It is not a judgment about the value of that work. A curated guide has to be answerable for what it recommends, and no small group of maintainers can be answerable across that whole span. Several of those areas already have their own community-curated resources. Where the boundary is genuinely porous, we cross-link rather than duplicate.

Editorial principles

The Guide is built on five commitments — these are reproduced in full on the How to use this guide page:

  1. No claim of completeness.
  2. Curation through community interest.
  3. Annotation is required.
  4. Complement INSPIRE-HEP and arXiv, do not compete with them.
  5. Sustainability by design.

Maintainers

The Living Guide is maintained by:

  • Claudius Krause — Marietta Blau Institute for Particle Physics, Austrian Academy of Sciences
  • Ramon Winterhalder — Università degli Studi di Milano & INFN Sezione di Milano
  • Matthew Feickert — University of Wisconsin–Madison
  • Benjamin Nachman — SLAC National Accelerator Laboratory & Stanford University

Section authors are credited individually on the sections they contribute. The maintainers coordinate and keep the infrastructure running, while the content belongs to the people who write it.

Acknowledgments

The Living Guide builds on six years of community contributions to the original Living Review. We thank everyone who contributed to the Review and who has helped shape this successor resource.