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Phenomenology

Section status

Contributor: Unclaimed — this section has no named author yet. See Contribute.

Last reviewed: —

Status: Stub — a starting point only. The curated list is not yet written.

Overview

Phenomenology connects theory to measurement: extracting the parameters of the Standard Model and its extensions from data, and turning theoretical input into predictions that can be confronted with it. This section covers the fitting and interpretation end of that work, including parton distribution functions and global fits, BSM parameter scans and reinterpretation, and symbolic approaches that produce formulae rather than opaque functions.

The machinery for generating predictions — scattering amplitudes, phase-space integration, parton showers and event generation — lives in Simulation & fast emulation instead. The boundary is between producing a prediction and inferring a parameter from one, and it was drawn there because the generation stages share an architecture and a set of failure modes with detector simulation, which makes them more useful read together than split by whether the physics is theoretical or experimental.

Fits are a natural home for machine learning because the objects being fitted are functions, not numbers, and because the parameter spaces are large enough that scanning them exhaustively is not an option.

  • Modern Machine Learning and Particle Physics Phenomenology at the LHC, Ubiali (2026) (arXiv:2602.03728) — modern machine learning and LHC phenomenology, the closest thing to a dedicated review
  • Lecture notes on Machine Learning applications for global fits, Alda (2026) (arXiv:2604.07520) — lecture notes on machine learning for global fits
  • Parton distribution functions, Forte et al. (2020) (arXiv:2008.12305) — a review of parton distribution functions, for the physics context the ML work sits in

Curated paper list

This list is a seed, not a curated selection

A few landmark papers are listed to give the section a starting shape. A proper curated list — thematically grouped, with an annotation on every entry — is what this section still needs. See Contribute.

  • An open-source machine learning framework for global analyses of parton distributions, Ball et al. (2021) (arXiv:2109.02671) — an open-source ML framework for global parton distribution analyses, the reference implementation in the area
  • Back to the Formula -- LHC Edition, Butter et al. (2021) (arXiv:2109.10414) — symbolic regression applied to LHC observables, producing formulae rather than black-box functions
  • Deep Learning Analysis of Deeply Virtual Exclusive Photoproduction, Grigsby et al. (2020) (arXiv:2012.04801) — deep learning applied to deeply virtual exclusive photoproduction, representative of the extraction problems in this area

Benchmarks, datasets & software

Not yet compiled for this section.

Open questions

What is settled here, what is contested, and what remains unsolved? This is the part a bibliography structurally cannot provide, and often the most useful paragraph on the page.

Further reading

Relevant work that is not an entry point — too specialized, too recent, or simply not where a newcomer should start. Suggestions that do not fit the curated list above belong here rather than being turned away.

Nothing listed yet.

Cross-references