Hasil untuk "Physics"

C. Brangwynne, P. Tompa, R. Pappu

F. An, G. An, Q. An et al.

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose underground liquid scintillator detector, was proposed with the determination of the neutrino mass hierarchy (MH) as a primary physics goal. The excellent energy resolution and the large fiducial volume anticipated for the JUNO detector offer exciting opportunities for addressing many important topics in neutrino and astro-particle physics. In this document, we present the physics motivations and the anticipated performance of the JUNO detector for various proposed measurements. Following an introduction summarizing the current status and open issues in neutrino physics, we discuss how the detection of antineutrinos generated by a cluster of nuclear power plants allows the determination of the neutrino MH at a 3–4σ significance with six years of running of JUNO. The measurement of antineutrino spectrum with excellent energy resolution will also lead to the precise determination of the neutrino oscillation parameters ${\mathrm{sin}}^{2}{\theta }_{12}$, ${\rm{\Delta }}{m}_{21}^{2}$, and $| {\rm{\Delta }}{m}_{{ee}}^{2}| $ to an accuracy of better than 1%, which will play a crucial role in the future unitarity test of the MNSP matrix. The JUNO detector is capable of observing not only antineutrinos from the power plants, but also neutrinos/antineutrinos from terrestrial and extra-terrestrial sources, including supernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos, atmospheric neutrinos, and solar neutrinos. As a result of JUNO's large size, excellent energy resolution, and vertex reconstruction capability, interesting new data on these topics can be collected. For example, a neutrino burst from a typical core-collapse supernova at a distance of 10 kpc would lead to ∼5000 inverse-beta-decay events and ∼2000 all-flavor neutrino–proton ES events in JUNO, which are of crucial importance for understanding the mechanism of supernova explosion and for exploring novel phenomena such as collective neutrino oscillations. Detection of neutrinos from all past core-collapse supernova explosions in the visible universe with JUNO would further provide valuable information on the cosmic star-formation rate and the average core-collapse neutrino energy spectrum. Antineutrinos originating from the radioactive decay of uranium and thorium in the Earth can be detected in JUNO with a rate of ∼400 events per year, significantly improving the statistics of existing geoneutrino event samples. Atmospheric neutrino events collected in JUNO can provide independent inputs for determining the MH and the octant of the ${\theta }_{23}$ mixing angle. Detection of the (7)Be and (8)B solar neutrino events at JUNO would shed new light on the solar metallicity problem and examine the transition region between the vacuum and matter dominated neutrino oscillations. Regarding light sterile neutrino topics, sterile neutrinos with ${10}^{-5}\,{{\rm{eV}}}^{2}\lt {\rm{\Delta }}{m}_{41}^{2}\lt {10}^{-2}\,{{\rm{eV}}}^{2}$ and a sufficiently large mixing angle ${\theta }_{14}$ could be identified through a precise measurement of the reactor antineutrino energy spectrum. Meanwhile, JUNO can also provide us excellent opportunities to test the eV-scale sterile neutrino hypothesis, using either the radioactive neutrino sources or a cyclotron-produced neutrino beam. The JUNO detector is also sensitive to several other beyondthe-standard-model physics. Examples include the search for proton decay via the $p\to {K}^{+}+\bar{\nu }$ decay channel, search for neutrinos resulting from dark-matter annihilation in the Sun, search for violation of Lorentz invariance via the sidereal modulation of the reactor neutrino event rate, and search for the effects of non-standard interactions. The proposed construction of the JUNO detector will provide a unique facility to address many outstanding crucial questions in particle and astrophysics in a timely and cost-effective fashion. It holds the great potential for further advancing our quest to understanding the fundamental properties of neutrinos, one of the building blocks of our Universe.

J. Monteith, W. E. Reifsnyder

Zhen Wang, C. Bauch, S. Bhattacharyya et al.

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination - one of the most important preventive measures of modern times - is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.

G. Mavko, T. Mukerji, J. Dvorkin

F. Krausz

D. Chowdhury, L. Santen, A. Schadschneider

Abstract In the so-called “microscopic” models of vehicular traffic, attention is paid explicitly to each individual vehicle each of which is represented by a “particle”; the nature of the “interactions” among these particles is determined by the way the vehicles influence each others’ movement. Therefore, vehicular traffic, modeled as a system of interacting “particles” driven far from equilibrium, offers the possibility to study various fundamental aspects of truly nonequilibrium systems which are of current interest in statistical physics. Analytical as well as numerical techniques of statistical physics are being used to study these models to understand rich variety of physical phenomena exhibited by vehicular traffic. Some of these phenomena, observed in vehicular traffic under different circumstances, include transitions from one dynamical phase to another, criticality and self-organized criticality, metastability and hysteresis, phase-segregation, etc. In this critical review, written from the perspective of statistical physics, we explain the guiding principles behind all the main theoretical approaches. But we present detailed discussions on the results obtained mainly from the so-called “particle-hopping” models, particularly emphasizing those which have been formulated in recent years using the language of cellular automata.

J. Cardy

H. Wiedemann

J. Seinfeld

C. Amsler, P. Richardson, O. Zenin et al.

S. Kak

Z. Chi, Yingchao Du, Wenhui Huang et al.

P. Chaikin, T. Lubensky

M. Hillery, R. F. O’Connell, M. Scully et al.

Anosov Diffeomorphisms, J. Almeida, Alberto A. Pinto

R. Kamien

Jonathan Shlomi, P. Battaglia, J. Vlimant

Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs—sets of elements and their pairwise relations—and are a central method within the broader field of geometric deep learning. They are very expressive and have demonstrated superior performance to other classical deep learning approaches in a variety of domains. The data in particle physics are often represented by sets and graphs and as such, graph neural networks offer key advantages. Here we review various applications of graph neural networks in particle physics, including different graph constructions, model architectures and learning objectives, as well as key open problems in particle physics for which graph neural networks are promising.

M. Bardoscia, P. Barucca, S. Battiston et al.

As the total value of the global financial market outgrew the value of the real economy, financial institutions created a global web of interactions that embodies systemic risks. Understanding these networks requires new theoretical approaches and new tools for quantitative analysis. Statistical physics contributed significantly to this challenge by developing new metrics and models for the study of financial network structure, dynamics, and stability and instability. In this Review, we introduce network representations originating from different financial relationships, including direct interactions such as loans, similarities such as co-ownership and higher-order relations such as contracts involving several parties (for example, credit default swaps) or multilayer connections (possibly extending to the real economy). We then review models of financial contagion capturing the diffusion and impact of shocks across each of these systems. We also discuss different notions of ‘equilibrium’ in economics and statistical physics, and how they lead to maximum entropy ensembles of graphs, providing tools for financial network inference and the identification of early-warning signals of system-wide instabilities. The interconnectedness of the financial system is increasing over time, and modelling it as a network captures key interactions between financial institutions. This Review surveys the most successful applications of statistical physics and complex networks to the description and understanding of financial networks. Modelling the financial system as a network is crucial to capture the complex interactions between financial institutions. Such a network is naturally a time-dependent multiplex, because relationships between financial institutions are of many different kinds and keep changing. Models of financial contagion enable understanding of how shocks propagate from one financial institution to another. Missing information on financial networks can be ‘reconstructed’ using maximum entropy approaches borrowed from statistical mechanics. Techniques based on financial networks have been adopted by practitioners and policy institutions. Modelling the financial system as a network is crucial to capture the complex interactions between financial institutions. Such a network is naturally a time-dependent multiplex, because relationships between financial institutions are of many different kinds and keep changing. Models of financial contagion enable understanding of how shocks propagate from one financial institution to another. Missing information on financial networks can be ‘reconstructed’ using maximum entropy approaches borrowed from statistical mechanics. Techniques based on financial networks have been adopted by practitioners and policy institutions.

G. Rempe, M. Scully

This book contains information on atomic, molecular and optical physics. Topics covered include: muon-catalyzed fusion and cooperative effects in atomic physics.