The European Physical Journal Special Topics最新文献

This paper examines the issue of designing an extended dissipative state estimator for a class of neural networks with multiple time-varying delays. The novelty of this problem lies in assuming distinct time-varying delays for each node, demonstrating its generalizability and complexity. An event-triggered state estimator with a known output measurement is proposed to facilitate these targeted network responses by saving limited communication resources. Consequently, sufficient conditions for an extended dissipative estimator have been achieved by constructing an augmented Lyapunov–Krasovskii functional (LKF) and finding its derivative. A generalized free-weighting matrix inequality (GFWMI) is utilized to achieve a tighter upper bound of the derivative, leading to a less conservative result in linear matrix inequalities (LMIs). Ultimately, a numerical example is shown to verify the advantages and efficacy of the main findings.

The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review provides a detailed examination of current and past ML techniques in this domain. It starts by exploring various data representation methods and ML architectures, encompassing traditional ML algorithms and advanced deep learning techniques. Subsequent sections discuss specific instances of successful ML applications in jet flavor tagging in the ATLAS and CMS experiments at the LHC, ranging from basic fully-connected layers to graph neural networks employing attention mechanisms. To systematically categorize the advancements over the LHC’s three runs, the paper classifies jet tagging algorithms into three generations, each characterized by specific data representation techniques and ML architectures. This classification aims to provide an overview of the chronological evolution in this field. Finally, a brief discussion about anticipated future developments and potential research directions in the field is presented.

The study of oscillator networks is currently the subject of intensive efforts for researchers working in the field of non-linear science. In this article, we are interested in the collective behavior of a star network formed by four mutually coupled Rayleigh–Duffing type oscillators (RDO here after) although each isolated oscillator undergoes a fixed point motion. The coupling considered is non-linear but exploits the intrinsic non-linearity of each of the oscillators so that no non-linear coupling function is necessary as usual. Using analytical techniques, the basic properties of the star system are studied in terms of equilibrium points and their stability, conditions for the appearance of Hopf bifurcations, dissipation and existence of attractors. The direct numerical integration of the mathematical model highlights fascinating phenomena, such as the coexistence of several parallel bifurcation branches, the coexistence of dynamics of the same type or of different types (i.e., regular and chaotic, hidden or self-excited) as well as multi-spiral chaos. These features are uncovered when changing both initial conditions and parameters. The tests carried out in the laboratory using the Arduino module show very good agreement with the results of the theoretical analysis. The study conducted out in this article provides valuable information as a prelude to understanding the behavior of a much more complex network of Rayleigh–Duffing type oscillators and Gunn type microwave oscillators as well.

This article attempts to summarize the effort by the particle physics community in addressing the tedious work of determining the parameter spaces of beyond-the-standard-model (BSM) scenarios, allowed by data. These spaces, typically associated with a large number of dimensions, especially in the presence of nuisance parameters, suffer from the curse of dimensionality and thus render naive sampling of any kind—even the computationally inexpensive ones—ineffective. Over the years, various new sampling (from variations of Markov Chain Monte Carlo (MCMC) to dynamic nested sampling) and machine learning (ML) algorithms have been adopted by the community to alleviate this issue. If not all, we discuss potentially the most important ones among them and the significance of their results, in detail.

In light of recent measurements suggesting potential lepton flavor universality violations in semileptonic decays at collider experiments, this article provides a concise study of tree- and loop-level B-hadron semileptonic decays, (b rightarrow c l nu _l) and (b rightarrow s l^+ l^-). We provide predictions for lepton flavor violating observables, ({mathcal {R}}_{J/psi }) and ({mathcal {R}}_{eta _c}), across the entire (q^2) range. Our study employs the Relativistic Independent Quark Model (RIQM), highlighting a model-dependent approach to these observables. We compare our model’s predictions with existing lattice predictions, demonstrating the strong applicability of the RIQM framework in describing (B_c) decays. Additionally, we reassess global averages for ({mathcal {R}}_{D(D^*)}) and ({mathcal {R}}_{K(K^*)}) in semileptonic transitions. With the upcoming experimental upgrades and the anticipated Run 3 data on (B_c) meson decays, rapid confirmation of these quantities could indicate significant evidence of physics beyond the Standard Model, thereby opening new pathways for understanding the complex flavor dynamics in B meson decays.