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This Booklet is extracted from the Review of Particle Physics, Physical Review D, 98, 030001 (2018). This Particle Physics Booklet includes the Summary Tables plus essential tables, figures, and equations from selected review articles. The table of contents, on the following pages, lists also additional material available in the full Review.
Promising experiments from the moon using particle detectors are discussed, noting the advantage of the large flux collecting power Pc offered by the remote, stable environment of a lunar base. An observatory class of particle experiments is presented, based upon proposals at NASA's recent Stanford workshop. They vary from neutrino astronomy, particle astrophysics, and cosmic ray experiments to space physics and fundamental physics experiments such as proton decay and 'table-top' arrays. This research is background-limited on earth, and it is awkward and unrealistic in earth orbit, but is particularly suited for the moon where Pc can be quite large and the instrumentation is not subject to atmospheric erosion as it is (for large t) in low earth orbit.
The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,324 new measurements from 878 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on High Energy Soft QCD and Diffraction and one on the Determination of CKM Angles from B Hadrons. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 98 review articles. Volume 2 consists of the Particle Listings and contains also 22 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Pariicle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print and as a web version optimized for use on phones as well as an Android app.
Foundation models have revolutionized artificial intelligence, with Large Language Models demonstrating unprecedented capabilities in multimodal understanding, reasoning and tool use. Nuclear and particle physics stands at a critical juncture where similar transformative potential awaits realization. The field generates exabytes of experimental data, exascale simulations, and decades of theoretical insights — yet these remain largely disconnected from modern Artifical Intelligence (AI) capabilities, with most physics AI applications confined to narrow, task-specific models that suffer from domain shifting when applied to real experimental data. We present a roadmap for FM4NPP (Foundation Model for Nuclear and Particle Physics), systematically scaling from current proof-of-concept models to trillion-parameter architectures capable of autonomous discovery. Our approach advances three critical frontiers: unified data infrastructure integrating detector data, scientific knowledge and computational tools across global facilities; multi-facility foundation models enabling cross-experiment knowledge transfer and accelerated discovery; and agentic AI capabilities for reasoning and autonomous tool use. The resulting self-evolving FM4NPP will transform physics research by converting time-intensive data analysis, theory derivation and computational bottlenecks into rapid AI–human collaborative discovery. This paradigm shift promises to fundamentally accelerate scientific progress in nuclear and particle physics, enabling researchers to focus on high-level insights while AI handles routine analysis and explores vast parameter spaces beyond human capacity.
The pursuit of knowledge in particle physics requires constant learning. As new tools become available, new theories are developed, and physicists search for new answers with ever-evolving methods. However, it is the case that formal educational systems serve as the primary training grounds for particle physicists. Graduate school (and undergraduate school to a lesser extent) is where researchers learn most of the technical skills required for research, develop scientific problem-solving abilities, learn how to establish themselves in their field, and begin developing their career. It is unfortunate, then, that the skills gained by physicists during their formal education are often mismatched with the skills actually required for a successful career in physics. We performed a survey of the U.S. particle physics community to determine the missing elements of graduate and undergraduate education and to gauge how to bridge these gaps. In this contributed paper, part of the 2021-22 Snowmass Community Planning Exercise, we report the results of this survey. We also recommend several specific community actions to improve the quality of particle physics education; the "community" here refers to physics departments, national labs, professional societies, funding agencies, and individual physicists.
We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-cloud encoder with standardized preprocessing, matched sampling, and a consistent evaluation protocol on a jet classification dataset. We compare contrastive (supervised and self-supervised), masked particle modeling, and generative reconstruction objectives under a common training regimen. In addition, we introduce targeted supervised architectural modifications that achieve state-of-the-art performance on benchmark evaluations. This controlled comparison isolates the contributions of the learning objective, highlights their respective strengths and limitations, and provides reproducible baselines. We position this work as a reference point for the future development of foundation models in particle physics, enabling more transparent and robust progress across the community.
This Snowmass2021 Contributed Paper addresses the role of the Particle Physics community in creating and fostering international connections in American education. It describes the pressing need to introduce students and faculty to the challenges and rewards of international collaboration, not only to develop the next generation of scientists and engineers for particle physics, but to maintain and build U.S. leadership on an increasingly competitive world stage. We present and assess current efforts in education and public engagement with an eye toward identifying those activities in need of change or increased resources to improve audience reach and program efficacy. We also consider possible new activities that might improve upon or complement existing programs, with the common goal of providing all U.S. students with the opportunity to benefit from a quality international scientific experience.
The mystery associated with a proposed Dark Sector of phenomena that are separate from the standard model of particle physics is described. A Dark Sector may possess matter particles, force carriers which mediate their interactions, and new interactions and symmetries that are beyond the standard model of particle physics. Various approaches for Dark Sector searches are described, including those at the energy frontier at the Large Hadron Collider, in astrophysical interactions with both terrestrial experiments and those in space-born platforms. Searches using low energy photons from microwave energies in cryogenic environments to x-ray energies are also described. While there is no noncontroversial evidence for Dark Sector phenomena presently, new searches with more modern equipment and analysis methods are exploring regions of phase space that have not been available before now, indicating ongoing interest and excitement in this research.
The interplay between cosmology and elementary particle physics is discussed. The standard cosmology is reviewed, concentrating on primordial nucleosynthesis and discussing how the standard cosmology has been used to place constraints on the properties of various particles. Baryogenesis is discussed, showing how a scenario in which the B-, C-, and CP-violating interactions in GUTs provide a dynamical explanation for the predominance of matter over antimatter and for the present baryon-to-photon ratio. It is shown how the very early dynamical evolution of a very weakly coupled scalar field which is initially displaced from the minimum of its potential may explain a handful of very fundamental cosmological facts which are not explained by the standard cosmology.
The Particle Physics Division (PPD) at Fermilab needed to modernize its outdated lifting fixture database. Many fixtures lacked identification or had incomplete information. During a summer internship, I cross-referenced existing data, photographed and measured fixtures using standardized tools, and updated their locations and details in a modern Excel format. Some fixtures were identified as belonging to other departments, like the Applied Physics and Superconducting Technology Directorate (APS-TD). This effort significantly improved the accuracy and utility of the PPD database, streamlined other departmental inventories, and potentially saved hundreds of thousands of dollars. The work concluded with a clear, organized record of all verified lifting fixtures.
Diversity, Equity, Inclusion, and Accessibility (DEIA) are not only called for to ensure morality and justice in our society, they also support ongoing and future excellence in particle physics. Over the past decade, the particle physics community has devised programs to support DEIA along multiple axes, and the way we think about measuring and implementing these initiatives has evolved. DEIA in physics is a broad topic, so in this paper we focus on the experiences of marginalized communities and outline ways different stakeholders can build a culture of equitable access for the success of marginalized individuals. Specifically, we identify urgent needs in the following areas: (1) We need to acquire a better understanding of the status quo, both quantitatively and qualitatively, to assess the effectiveness of existing programs and to develop best practices; (2) we need to develop effective and inclusive ways to engage marginalized communities; (3) we need to create infrastructure to better support members of marginalized communities, on an academic, financial and personal level; (4) we need to create an environment conducive to equitable access and success by establishing community expectations, fostering inclusion in social interactions, and holding individuals and institutions accountable; and (5) we need to establish a mechanism to monitor progress in the area of DEIA, including the implementation of the recommendations enumerated in this paper and others during the Snowmass 2021 process.
These reports present the results of the 2013 Community Summer Study of the APS Division of Particles and Fields ("Snowmass 2013") on the future program of particle physics in the U.S. Chapter 8, on the Instrumentation Frontier, discusses the instrumentation needs of future experiments in the Energy, Intensity, and Cosmic Frontiers, promising new technologies for particle physics research, and issues of gathering resources for long-term research in this area.
These reports present the results of the 2013 Community Summer Study of the APS Division of Particles and Fields ("Snowmass 2013") on the future program of particle physics in the U.S. Chapter 8, on the Instrumentation Frontier, discusses the instrumentation needs of future experiments in the Energy, Intensity, and Cosmic Frontiers, promising new technologies for particle physics research, and issues of gathering resources for long-term research in this area.
Accelerated Machine Learning as a Service for Particle Physics Computing: • Amount and complexity of high-energy physics data increases dramatically from 2025 onward • Traditional algorithms will require too much CPU time • Machine learning can solve combinatorially-scaling problems in constant time, but must be fast enough
Recently, much attention has been focused on the replicability of scientific results, causing scientists, statisticians, and journal editors to examine closely their methodologies and publishing criteria. Experimental particle physicists have been aware of the precursors of non-replicable research for many decades and have many safeguards to ensure that the published results are as reliable as possible. The experiments require large investments of time and effort to design, construct, and operate. Large collaborations produce and check the results, and many papers are signed by more than three thousand authors. This paper gives an introduction to what experimental particle physics is and to some of the tools that are used to analyze the data. It describes the procedures used to ensure that results can be computationally reproduced, both by collaborators and by non-collaborators. It describes the status of publicly available data sets and analysis tools that aid in reproduction and recasting of experimental results. It also describes methods particle physicists use to maximize the reliability of the results, which increases the probability that they can be replicated by other collaborations or even the same collaborations with more data and new personnel. Examples of results that were later found to be false are given, both with failed replication attempts and one with alarmingly successful replications. While some of the characteristics of particle physics experiments are unique, many of the procedures and techniques can be and are used in other fields.
We present a minimal extension of the Standard Model (SM) providing a consistent picture of particle physics from the electroweak scale to the Planck scale and of cosmology from inflation until today. Three right-handed neutrinos N {sub i} , a new color triplet Q and a complex SM-singlet scalar σ, whose vacuum expectation value v {sub σ} ∼ 10{sup 11} GeV breaks lepton number and a Peccei-Quinn symmetry simultaneously, are added to the SM. At low energies, the model reduces to the SM, augmented by seesaw generated neutrino masses and mixing, plus the axion. The latter solves the strong CP problem and accounts for the cold dark matter in the Universe. The inflaton is comprised by a mixture of σ and the SM Higgs, and reheating of the Universe after inflation proceeds via the Higgs portal. Baryogenesis occurs via thermal leptogenesis. Thus, five fundamental problems of particle physics and cosmology are solved at one stroke in this unified Standard Model—axion—seesaw—Higgs portal inflation (SMASH) model. It can be probed decisively by upcoming cosmic microwave background and axion dark matter experiments.
Presently under construction in Lund, Sweden, the European Spallation Source (ESS) will be the world’s brightest neutron source. As such, it has the potential for a particle physics program with a unique reach and which is complementary to that available at other facilities. This paper describes proposed particle physics activities for the ESS. Furthermore, these encompass the exploitation of both the neutrons and neutrinos produced at the ESS for high precision (sensitivity) measurements (searches).
Large language models have revolutionized artificial intelligence by enabling large, generalizable models trained through self-supervision. This paradigm has inspired the development of scientific foundation models (FMs). However, applying this capability to experimental particle physics is challenging due to the sparse, spatially distributed nature of detector data, which differs dramatically from natural language. This work addresses if an FM for particle physics can scale and generalize across diverse tasks. We introduce a new dataset with more than 11 million particle collision events and a suite of downstream tasks and labeled data for evaluation. We propose a novel self-supervised training method for detector data and demonstrate its neural scalability with models that feature up to 188 million parameters. With frozen weights and task-specific adapters, this FM consistently outperforms baseline models across all downstream tasks. The performance also exhibits robust data-efficient adaptation. Further analysis reveals that the representations extracted by the FM are task-agnostic but can be specialized via a single linear mapping for different downstream tasks.