Particle physics
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Advances in Particle Physics: A Comprehensive Overview
Introduction to Particle Physics
Particle physics, also known as high-energy physics, is the branch of physics that studies the fundamental particles of the universe and the forces that govern their interactions. This field aims to understand the smallest detectable particles and the fundamental interactions necessary to explain their behavior7.
Standard Model and Beyond
The Standard Model of particle physics is a well-established theory that describes the electromagnetic, weak, and strong nuclear interactions, which are mediated by gauge bosons. It includes fundamental particles such as quarks, leptons, and the recently discovered Higgs boson1 2. Despite its success, the Standard Model does not account for phenomena such as dark matter, dark energy, and the matter-antimatter asymmetry in the universe6 9.
Higgs Boson and Gauge Bosons
The discovery of the Higgs boson at the Large Hadron Collider (LHC) was a significant milestone in particle physics, confirming the mechanism that gives mass to elementary particles1 2. The properties of gauge bosons, including the Higgs boson, are meticulously measured and summarized in various reviews and summary tables1 2.
Hypothetical Particles and New Physics
Researchers continue to search for hypothetical particles such as supersymmetric particles, heavy bosons, axions, and dark photons. These searches aim to uncover new physics beyond the Standard Model, which could explain the tiny neutrino masses, baryogenesis, and the phenomena of dark matter and dark energy1 2 6.
Quantum Technologies in Particle Physics
Quantum technologies are emerging as a new frontier in particle physics. Quantum simulators, inspired by Feynman's concept of quantum computing, are being developed to study non-perturbative dynamics in strong coupling regimes. These technologies hold promise for investigating complex problems like quark confinement and the properties of dense nuclear matter6.
Graph Neural Networks in Particle Physics
Graph neural networks (GNNs) are a powerful tool in particle physics, offering advantages in data representation and analysis. GNNs operate on graphs, which are sets of elements and their pairwise relations, making them well-suited for the complex data structures in particle physics. They have shown superior performance in various applications, including model architectures and learning objectives8.
Tabletop-Scale Experiments
While large-scale experiments like those at the LHC are crucial, smaller, laboratory-scale "tabletop" experiments also play a vital role in probing the frontiers of particle physics. These experiments use precision measurement techniques to detect tiny signals from new particles or forces, potentially leading to groundbreaking discoveries in fundamental physics9.
Conclusion
Particle physics continues to evolve, driven by both theoretical advancements and experimental discoveries. The Standard Model remains a cornerstone of our understanding, but the quest for new physics is ongoing. Innovations in quantum technologies and machine learning, along with both large-scale and tabletop experiments, are paving the way for future breakthroughs in this fascinating field.
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Most relevant research papers on this topic
Review of Particle Physics
The Review of Particle Physics summarizes particle physics and cosmology, listing properties and search limits for hypothetical particles, and providing a comprehensive overview of the field.
Literature Review
Highly CitedReview of Particle Physics (RPP)
This Review of Particle Physics summarizes particle properties and searches for hypothetical particles, providing a comprehensive overview of the field.
Highly CitedAPS : Review of Particle Physics, 2018
The complete Review(both volumes) is published online on the website of the Particle Data Group(http://pdg.lbl.gov) and in a journal. Volume 1 is available in print as thePDG Book. AParticle Physics Bookletwith the Summary Tables and essential tables, figures, and equations from selected review articles is also available.
Literature ReviewHighly CitedElementary Particle Physics
F. Gürsey, Lectures from the Istanbul Summer School of Theoretical Physics, 1964.
Highly CitedReview of Particle Physics
The APS Review of Particle Physics, 2018 provides a comprehensive overview of particle physics research, offering essential tables, figures, and equations for further reading.
Literature ReviewHighly CitedQuantum technologies in particle physics
Quantum technologies, such as quantum simulators, offer new insights into quark confinement and dense nuclear matter, potentially leading to new insights into the Standard Model and beyond.
Graph neural networks in particle physics
Graph neural networks show promise in particle physics, offering expressiveness and superior performance compared to other deep learning approaches.
Highly CitedProbing the frontiers of particle physics with tabletop-scale experiments
Tabletop-scale experiments using precision measurement techniques and devices from atomic, quantum, and condensed-matter physics may lead to discoveries in fundamental physics.
Highly CitedThe Particle Aspect of Meson Theory
The meson theory offers a promising approach to understanding the fundamental principles of elementary particles, with its main advantage being the absence of infinite self-energies and divergences.
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