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Review of Particle Physics

Open access
Nakamura, K·2010·Journal of Physics G: Nuclear and Particle Physics

Abstract

This biennial Review summarizes much of particle physics. Using data from previous editions, plus 2158 new measurements from 551 papers, we list, evaluate, and average measured properties of gauge bosons, leptons, quarks, mesons, and baryons. We also summarize searches for hypothetical particles such as Higgs bosons, heavy neutrinos, and supersymmetric particles. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as the Standard Model, particle detectors, probability, and statistics. Among the 108 reviews are many that are new or heavily revised including those on neutrino mass, mixing, and oscillations, QCD, top quark, CKM quark-mixing matrix, V ud & V us , V cb & V ub , fragmentation functions, particle detectors for accelerator and non-accelerator physics, magnetic monopoles, cosmological parameters, and big bang cosmology. A booklet is available containing the Summary Tables and abbreviated versions of some of the other sections of this full Review. All tables, listings, and reviews (and errata) are also available on the Particle Data Group website: pdg.lbl.gov.

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Metadata

Authors
Nakamura, K.
Venue
Journal of Physics G: Nuclear and Particle Physics
Published
2010-07-01
Volume
37
Issue
7A
Pages
075021
ISSN
  • 0954-3899 (ISSN-L)
  • 1361-6471
OpenAlex
W2165739256
Cited by
5,797
References
0
FWCI
789.18
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Review of Particle Physics. https://app.cochat.ai/papers/10.1088/0954-3899/37/7a/075021
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Nakamura, K. (2010). Review of Particle Physics. Journal of Physics G: Nuclear and Particle Physics. https://doi.org/10.1088/0954-3899/37/7a/075021

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.