Physics Reports最新文献

Our experience of the world around us is governed almost entirely by light–matter interactions. At the most fundamental level, such interactions are described by quantum electrodynamics (QED), a well-established theory that has stood up to decades of experimental testing to remarkable degrees of precision. However, the complexity of real systems almost always means that the quantum electrodynamical equations describing a given scenario are often infeasible or impractical to solve. Thus, a sequence of approximations and idealisations are made, in order to build up from the simple case of an isolated electron interacting with a gauge field leading to the deceptively simple laws governing reflection and refraction at mirrors and lenses. This review provides a pedagogical overview of this journey, concentrating on cases where external boundary conditions can be used as a control method. Beginning from the fundamental Lagrangian, topics include gauge freedom, perturbative macroscopic QED descriptions of spontaneous decay, Casimir–Polder forces, resonant energy transfer, interatomic Coulombic decay, all of which are described in terms of the dyadic Green’s tensor that solves the Helmholtz equation. We discuss in detail how to calculate this tensor in practical situations before outlining new techniques in the design and optimisation of perturbative light–matter interactions, highlighting some recent advances in free-form, unconstrained inverse design of optical devices. Finally, an outlook towards the frontiers in the interaction of quantum light with matter is given, including its interface with chemical reactivity via polaritonic chemistry and quantum chemistry via quantum electrodynamical density functional theory (QEDFT).

I describe the history of the calculations of NLO and NNLO QCD corrections to weak decays of mesons, particle–antiparticle mixing and electric dipole moments (EDMs) in the period 1988–2023. Also existing calculations of electroweak and QED corrections to these processes are included in this presentation. These efforts bear some analogies to the climbing of Himalayas and various expeditions by several teams of strongly motivated “climbers” allowed to move this field from the LO through the NLO to the NNLO level. We also summarize the most recent calculations within the Standard Model Effective Field Theory. The material is meant to be an up to date review of this very advanced field in non-technical terms as much as possible and a guide to the rich literature on NLO and NNLO corrections in question. In particular we stress for which processes these calculations are crucial for the tests of the Standard Model and to be able to differentiate between numerous New Physics models. It includes also several anecdotes related to the climbs that I was involved in. I hope that some of the comments made in the course of the presentation could turn out to be not only amusing but also instructive.

This paper provides a concise description of the free energy principle, starting from a formulation of random dynamical systems in terms of a Langevin equation and ending with a Bayesian mechanics that can be read as a physics of sentience. It rehearses the key steps using standard results from statistical physics. These steps entail (i) establishing a particular partition of states based upon conditional independencies that inherit from sparsely coupled dynamics, (ii) unpacking the implications of this partition in terms of Bayesian inference and (iii) describing the paths of particular states with a variational principle of least action. Teleologically, the free energy principle offers a normative account of self-organisation in terms of optimal Bayesian design and decision-making, in the sense of maximising marginal likelihood or Bayesian model evidence. In summary, starting from a description of the world in terms of random dynamical systems, we end up with a description of self-organisation as sentient behaviour that can be interpreted as self-evidencing; namely, self-assembly, autopoiesis or active inference.

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. These encompass the exploitation of both the neutrons and neutrinos produced at the ESS for high precision (sensitivity) measurements (searches).

When the universe is treated as a quantum system, it is described by a wave function. This wave function is a function not only of the matter fields, but also of spacetime. The no-boundary proposal is the idea that the wave function should be calculated by summing over geometries that have no boundary to the past, and over regular matter configurations on these geometries. Accordingly, the universe is finite, self-contained and the big bang singularity is avoided. Moreover, given a dynamical theory, the no-boundary proposal provides probabilities for various solutions of the theory. In this sense it provides a quantum theory of initial conditions.

This review starts with a general overview of the framework of quantum cosmology, describing both the canonical and path integral approaches, and their interpretations. After recalling several heuristic motivations for the no-boundary proposal, its consequences are illustrated with simple examples, mainly in the context of cosmic inflation. We review how to include perturbations, assess the classicality of spacetime and how probabilities may be derived. A special emphasis is given to explicit implementations in minisuperspace, to observational consequences, and to the relationship of the no-boundary wave function with string theory. At each stage, the required analytic and numerical techniques are explained in detail, including the Picard–Lefschetz approach to oscillating integrals.

Fluid dynamics turbulence refers to the chaotic and unpredictable dynamics of flows. Despite the fact that the equations governing the motion of fluids are known since more than two centuries, a comprehensive theory of turbulence is still a challenge for the scientific community. Rather recently a number of important breakthroughs have clarified many relevant, fascinating, and largely unexpected, statistical features of turbulent fluctuations. In these lectures, we discuss recent advances in the field with the aim of highlighting the physical meaning and implication of these new ideas and their role in contributing to disentangling different parts of our understanding of the turbulence problem. The lectures aim at introducing non-experts to the subject and no previous knowledge of the field is required.

We give a comprehensive review of recent developments on using the pure spinor formalism to compute massless superstring scattering amplitudes at tree level. The main results of the pure spinor computations are placed into the context of related topics including the color-kinematics duality in field theory and the mathematical structure of ${\alpha }^{\prime }$-corrections.

Chiral symmetry and its spontaneous breaking play an important role both in the light hadron and heavy hadron systems. The chiral perturbation theory ($\chi$PT) is the low energy effective field theory of the Quantum Chromodynamics. In this work, we shall review the investigations on the chiral corrections to the properties of the heavy mesons and baryons within the framework of $\chi$PT. We will also review the scatterings of the light pseudoscalar mesons and heavy hadrons, through which many new resonances such as the $D_{s0}^{\ast }\left(2317\right)$ could be understood.

Moreover, many new hadron states were observed experimentally in the past decades. A large group of these states is near-threshold resonances, such as the charged charmoniumlike $Z_c$ and $Z_{cs}$ states, bottomoniumlike $Z_b$ states, hidden-charm pentaquark $P_c$ and $P_{cs}$ states and the doubly charmed $T_{cc}$ state, etc. They are very good candidates of the loosely bound molecular states composed of a pair of charmed (bottom) hadrons, which are very similar to the loosely bound deuteron. The modern nuclear force was built upon the chiral effective field theory ($\chi$EFT), which is the extension of the $\chi$PT to the systems with two matter fields. The long-range and medium-long-range interactions between two nucleons arise from the single- and double-pion exchange respectively, which are well constrained by the chiral symmetry and its spontaneous breaking. The short-distance interactions can be described by the low energy constants. Such a framework works very well for the nucleon–nucleon scattering and nuclei. In this work, we will perform an extensive review of the progress on the heavy hadronic molecular states within the framework of $\chi$EFT. We shall emphasize that the same chiral dynamics not only govern the nuclei and forms the deuteron, but also dictates the shallow bound states or resonances composed of two heavy hadrons.

All beauty, richness and harmony in the emergent dynamics of a complex system largely depend on the specific way in which its elementary components interact. The last twenty-five years have seen the birth and development of the multidisciplinary field of Network Science, wherein a variety of distributed systems in physics, biology, social sciences and engineering have been modeled as networks of coupled units, in the attempt to unveil the mechanisms underneath their observed functionality. There is, however, a fundamental limit to such a representation: networks capture only pairwise interactions, whereas the functioning of many real-world systems not only involves dyadic connections, but rather is the outcome of collective actions at the level of groups of nodes. For instance, in ecological systems, three or more species may compete for food or territory, and similar multi-component interactions appear in functional and structural brain networks, protein interaction networks, semantic networks, multi-authors scientific collaborations, offline and online social networks, gene regulatory networks and spreading of consensus or contagious diseases due to multiple, simultaneous, contacts. Such multi-component interactions can only be grasped through either hypergraphs or simplicial complexes, which indeed have recently found a huge number of applications. In this report, we cover the extensive literature of the past years on this subject, and we focus on the structure and dynamics of hypergraphs and simplicial complexes. These are indeed becoming increasingly relevant, thanks to the enhanced resolution of data sets and the recent advances in data analysis techniques, which (concurrently and definitely) have shown that such structures play a pivotal role in the complex organization and functioning of real-world distributed systems.

Signal propagation in complex networks drives epidemics, is responsible for information going viral, promotes trust and facilitates moral behavior in social groups, enables the development of misinformation detection algorithms, and it is the main pillar supporting the fascinating cognitive abilities of the brain, to name just some examples. The geometry of signal propagation is determined as much by the network topology as it is by the diverse forms of nonlinear interactions that may take place between the nodes. Advances are therefore often system dependent and have limited translational potential across domains. Given over two decades worth of research on the subject, the time is thus certainly ripe, indeed the need is urgent, for a comprehensive review of signal propagation in complex networks. We here first survey different models that determine the nature of interactions between the nodes, including epidemic models, Kuramoto models, diffusion models, cascading failure models, and models describing neuronal dynamics. Secondly, we cover different types of complex networks and their topologies, including temporal networks, multilayer networks, and neural networks. Next, we cover network time series analysis techniques that make use of signal propagation, including network correlation analysis, information transfer and nonlinear correlation tools, network reconstruction, source localization and link prediction, as well as approaches based on artificial intelligence. Lastly, we review applications in epidemiology, social dynamics, neuroscience, engineering, and robotics. Taken together, we thus provide the reader with an up-to-date review of the complexities associated with the network’s role in propagating signals in the hope of better harnessing this to devise innovative applications across engineering, the social and natural sciences as well as to inspire future research.