Hasil untuk "Physics"

R. Carey

E. Merzbacher

M. Segev, Y. Silberberg, D. Christodoulides

D. Anderle, V. Bertone, Xu Cao et al.

Lepton scattering is an established ideal tool for studying inner structure of small particles such as nucleons as well as nuclei. As a future high energy nuclear physics project, an Electron-ion collider in China (EicC) has been proposed. It will be constructed based on an upgraded heavy-ion accelerator, High Intensity heavy-ion Accelerator Facility (HIAF) which is currently under construction, together with a new electron ring. The proposed collider will provide highly polarized electrons (with a polarization of ∼80%) and protons (with a polarization of ∼70%) with variable center of mass energies from 15 to 20 GeV and the luminosity of (2–3) × 1033 cm−2 · s−1. Polarized deuterons and Helium-3, as well as unpolarized ion beams from Carbon to Uranium, will be also available at the EicC. The main foci of the EicC will be precision measurements of the structure of the nucleon in the sea quark region, including 3D tomography of nucleon; the partonic structure of nuclei and the parton interaction with the nuclear environment; the exotic states, especially those with heavy flavor quark contents. In addition, issues fundamental to understanding the origin of mass could be addressed by measurements of heavy quarkonia near-threshold production at the EicC. In order to achieve the above-mentioned physics goals, a hermetical detector system will be constructed with cutting-edge technologies. This document is the result of collective contributions and valuable inputs from experts across the globe. The EicC physics program complements the ongoing scientific programs at the Jefferson Laboratory and the future EIC project in the United States. The success of this project will also advance both nuclear and particle physics as well as accelerator and detector technology in China.

D. Broido, M. Malorny, Gerd Birner et al.

I. Antcheva, M. Ballintijn, B. Bellenot et al.

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efcient way. Any instance of a C++ class can be stored into a ROOT le in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of les

J. Alimena, James Beacham, M. Borsato et al.

Particles beyond the Standard Model (SM) can generically have lifetimes that are long compared to SM particles at the weak scale. When produced at experiments such as the Large Hadron Collider (LHC) at CERN, these long-lived particles (LLPs) can decay far from the interaction vertex of the primary proton-proton collision. Such LLP signatures are distinct from those of promptly decaying particles that are targeted by the majority of searches for new physics at the LHC, often requiring customized techniques to identify, for example, significantly displaced decay vertices, tracks with atypical properties, and short track segments. Given their non-standard nature, a comprehensive overview of LLP signatures at the LHC is beneficial to ensure that possible avenues of the discovery of new physics are not overlooked. Here we report on the joint work of a community of theorists and experimentalists with the ATLAS, CMS, and LHCb experiments --- as well as those working on dedicated experiments such as MoEDAL, milliQan, MATHUSLA, CODEX-b, and FASER --- to survey the current state of LLP searches at the LHC, and to chart a path for the development of LLP searches into the future, both in the upcoming Run 3 and at the High-Luminosity LHC. The work is organized around the current and future potential capabilities of LHC experiments to generally discover new LLPs, and takes a signature-based approach to surveying classes of models that give rise to LLPs rather than emphasizing any particular theory motivation. We develop a set of simplified models; assess the coverage of current searches; document known, often unexpected backgrounds; explore the capabilities of proposed detector upgrades; provide recommendations for the presentation of search results; and look towards the newest frontiers, namely high-multiplicity "dark showers", highlighting opportunities for expanding the LHC reach for these signals.

Shabir Barzanjeh, A. Xuereb, S. Gröblacher et al.

The ability to control the motion of mechanical systems through interaction with light has opened the door to a plethora of applications in fundamental and applied physics. With experiments routinely reaching the quantum regime, the focus has now turned towards creating and exploiting interesting non-classical states of motion and entanglement in optomechanical systems. Quantumness has also shifted from being the very reason why experiments are constructed to becoming a resource for the investigation of fundamental physics and the creation of quantum technologies. Here, by focusing on opto- and electromechanical platforms we review recent progress in quantum state preparation and entanglement of mechanical systems, together with applications to signal processing and transduction, quantum sensing and topological physics, as well as small-scale thermodynamics. Interaction with light can be used to precisely control motional states. This Review surveys recent progress in the preparation of non-classical mechanical states and in the application of optomechanical platforms to specific tasks in quantum technology.

S. Incerti, S. Incerti, I. Kyriakou et al.

This Special Report presents a description of Geant4-DNA user applications dedicated to the simulation of track structures (TS) in liquid water and associated physical quantities (e.g., range, stopping power, mean free path…). These example applications are included in the Geant4 Monte Carlo toolkit and are available in open access. Each application is described and comparisons to recent international recommendations are shown (e.g., ICRU, MIRD), when available. The influence of physics models available in Geant4-DNA for the simulation of electron interactions in liquid water is discussed. Thanks to these applications, the authors show that the most recent sets of physics models available in Geant4-DNA (the so-called "option4" and "option 6" sets) enable more accurate simulation of stopping powers, dose point kernels, and W-values in liquid water, than the default set of models ("option 2") initially provided in Geant4-DNA. They also serve as reference applications for Geant4-DNA users interested in TS simulations.

C. Bierlich, Andy Buckley, J. Butterworth et al.

First released in 2010, the Rivet library forms an important repository for analysis code, facilitating comparisons between measurements of the final state in particle collisions and theoretical calculations of those final states. We give an overview of Rivet's current design and implementation, its uptake for analysis preservation and physics results, and summarise recent developments including propagation of MC systematic-uncertainty weights, heavy-ion and ep physics, and systems for detector emulation. In addition, we provide a short user guide that supplements and updates the Rivet user manual.

Igor Kaganovich, Michael Tendler

Partially ionized plasma physics has attracted a lot of attention recently due to numerous technological applications made possible by the increased sophistication of computer modelling, the depth of the theoretical analysis, and the technological applications to a vast field of the manufacturing for computer components. The partially ionized plasma is characterized by a significant presence of neutral particles in contrast to fully ionized plasma. The theoretical analysis is based upon solutions of the kinetic Boltzmann equation yielding the non-Maxwellian electron energy distribution function (EEDF) thereby emphasizing the difference with a fully ionized plasma. The impact of the effect on discharges in inert and molecular gases is described in detail yielding the complex nonlinear phenomena in plasma self-organization. A few examples of such phenomena are given including the non-monotonic EEDFs in the discharge afterglow in mixture of argon with the molecular gas NF3; the explosive generation of cold electron populations in capacitive discharges, hysteresis of EEDF in inductively coupled plasmas. Recently, highly advanced computer codes were developed in order to address the outstanding problems of plasma technology. These developments are briefly described in general terms.

Gengke Lai, Yongguang Zhang

Abstract Canada experienced an unprecedented wildfire season in 2023. Here, we analyzed the exceptional scale, dominant driving factors, and potential impacts on permafrost of these wildfires using Moderate Resolution Imaging Spectroradiometer burned area (BA) observations and machine learning methods. We found that the 2023 coast‐to‐coast wildfires raged a staggering area of 13.02 Mha, more than seven times the historical average (2001–2022). The extreme wildfires were predominantly driven by record‐breaking drought, characterized by heightened atmospheric aridity and reduced precipitation, with specific water deficit thresholds triggering sharp increases in BA. Over 80% of the 2023 wildfires occurred in Canada's permafrost regions, with burned areas increasing more than six‐fold across extensive permafrost, including Arctic regions. These wildfires are expected to accelerate permafrost degradation and considerable carbon release from thawing, presenting previously overlooked risks. Our results emphasize the urgent need for immediate climatic action to mitigate climate change and pressures from wildfire and permafrost degradation.

Siyuan Liu, Yingchao Fan, Qi Hu et al.

Hyperspectral image (HSI) has more spectral information than conventional images, which helps to distinguish targets in a complex scene more accurately. However, HSI typically has a low spatial resolution, which limits their application scenarios. To achieve high-resolution HSI, we propose a spectral and spatial multiscale coupling fusion model (SSMSFuse) for hyperspectral and multispectral image (MSI). SSMSFuse couples the spatial information of MSI and the spectral information of HSI at multiscales by means of a two-branch network structure, thus obtaining the fused images with high spatial and spectral resolution. SSMSFuse consists of two branches, namely the spatial embedding network (Spa-Net) and the spectral embedding network (Spe-Net). Spa-Net is constructed using a multiscale convolutional neural network to better mine multilevel spatial features from MSI. Spe-Net is constructed using self-attention, which can model the long-distance spectral dependencies of HSI to better extract spectral information from HSI. Finally, to achieve interactive coupling of dual-branch information, we designed a spatial–spectral guidance fusion block to fuse features at different scales to avoid loss of spatial and spectral details. Experiments are carried out on four public datasets, and the results show that the proposed method can effectively improve the objective indicators of the fusion results, such as the peak signal to noise ratio, which is increased by 1.36%, and the root mean square error, which is increased by 9.72% on the CAVE dataset, and satisfactory subjective results are also obtained.

Masayuki Kano, Yusuke Tanaka, Daisuke Sato et al.

Abstract Monitoring and predicting fault slip behaviors in subduction zones is essential for understanding earthquake cycles and assessing future earthquake potential. We developed a data assimilation method for fault slip monitoring and the short-term prediction of slow slip events, and applied to the 2010 Bungo Channel slow slip event in southwest Japan. The observed geodetic data were quantitatively explained using a physics-based model with data assimilation. We investigated short-term predictability by assimilating observation data within limited periods. Without prior constraints on fault slip style, observations solely during slip acceleration predicted the occurrence of a fast slip; however, the inclusion of slip deceleration data successfully predicted a slow transient slip. With prior constraints to exclude unstable slip, the assimilation of data after slow slip event occurrence also predicted a slow transient slip. This study provides a tool using data assimilation for fault slip monitoring and prediction based on real observation data. Graphical Abstract

Luis A. Anchordoqui, Ignatios Antoniadis, Dieter Lüst

Over the last few years, low- and high-redshift observations set off tensions in the measurement of the present-day expansion rate H0 and in the determination of the amplitude of the matter clustering in the late Universe (parameterized by S8). It was recently noted that both these tensions can be resolved if the cosmological constant parametrizing the dark energy content switches its sign at a critical redshift zc∼2. However, the anti-de Sitter (AdS) swampland conjecture suggests that the postulated switch in sign of the cosmological constant at zero temperature seems unlikely because the AdS vacua are an infinite distance apart from de Sitter (dS) vacua in moduli space. We provide an explanation for the required AdS → dS crossover transition in the vacuum energy using the Casimir forces of fields inhabiting the bulk. We then use entropy arguments to claim that any AdS → dS transition between metastable vacua must be accompanied by a reduction of the species scale where gravity becomes strong. We provide a few examples supporting this AdS → dS uplift conjecture.

Juan Diego Toscano, Theo Käufer, Zhibo Wang et al.

We propose the Artificial Intelligence Velocimetry-Thermometry (AIVT) method to infer hidden temperature fields from experimental turbulent velocity data. This physics-informed machine learning method enables us to infer continuous temperature fields using only sparse velocity data, hence eliminating the need for direct temperature measurements. Specifically, AIVT is based on physics-informed Kolmogorov-Arnold Networks (not neural networks) and is trained by optimizing a combined loss function that minimizes the residuals of the velocity data, boundary conditions, and the governing equations. We apply AIVT to a unique set of experimental volumetric and simultaneous temperature and velocity data of Rayleigh-Bénard convection (RBC) that we acquired by combining Particle Image Thermometry and Lagrangian Particle Tracking. This allows us to compare AIVT predictions and measurements directly. We demonstrate that we can reconstruct and infer continuous and instantaneous velocity and temperature fields from sparse experimental data at a fidelity comparable to direct numerical simulations (DNS) of turbulence. This, in turn, enables us to compute important quantities for quantifying turbulence, such as fluctuations, viscous and thermal dissipation, and QR distribution. This paradigm shift in processing experimental data using AIVT to infer turbulent fields at DNS-level fidelity is a promising avenue in breaking the current deadlock of quantitative understanding of turbulence at high Reynolds numbers, where DNS is computationally infeasible.

Fabrizia Cilento, Barbara Palmieri, Giovangiuseppe Giusto et al.

In the aerospace sector, structural and non-structural composite components are usually subjected to a wide range of environmental conditions. Among all, moisture can seriously damage these materials’ performance, reducing their mechanical, thermal, electrical, and physical properties as well as their service time. Lightweight protective barrier coatings capable of reducing the diffusion of gases and/or liquids in a material can improve the material’s resistance in humid environments. In this work, nanolamellar nanocomposites characterized by a high in-plane orientation of nanoplatelets have been employed as protective coatings for Kevlar sandwich panels, reproducing the construction of a nacelle engine. The effectiveness of the protection against water uptake of nanocomposites reinforced with graphite nanoplatelets (GNPs) at high filler contents (70, 80 and 90 wt%) has been investigated using moisture uptake and Ground-Air-Ground (GAG) tests in an environmental chamber. GNP coatings effectively work as barrier by generating highly tortuous paths for molecule diffusion. Results showed a dependence of the absorption on the coating composition and inner structure. Films @70 wt% GNPs showed the best protection against moisture uptake by delaying the phenomenon and reducing the absorption by −80% after 3 days and −35% after 41 days.

Mohamed Mahmoud Sebbab, Abdelhadi El Ouahidi, Mehdi Ousbih et al.

The purpose of this paper is to identify, quantify and delineate the areas with suitable aggregate resources in the Precambrian massif of Ifni and the limestone plateau of Lakhssas (southwest Morocco). To fulfill this objective, a study was undertaken on the geotechnical parameters of the various geological outcrops of the region based on the analysis of 42 rock samples (carbonate, magmatic, detritic and volcano-detritic). Initially, we subjected these samples to a series of laboratory tests (impact resistance (L.A), wear resistance (MDE), density, porosity, absorption), to classify them according to geotechnical standards. Then, a geospatial database was created, to exploit these geotechnical data, from a geographical information system (GIS) to produce various thematic maps. Based on the results of this study, all geotechnical classes according to the standards (A to E for the European standard and 1A to 6D for the Moroccan standard) are present with good to very good geomechanical properties (L.A between 12% and 35%, MDE between 5% and 30%). This classification allowed us to use GIS to identify and quantify potential areas for exploitation by assigning five categories of geotechnical suitability levels (high (4), medium (3), low (2), very low (1) and others (0)) and to show that approximately 72% of the study area belongs to the categories high, medium and low. The combination of laboratory results and GIS has allowed us to carry out geotechnical mapping that will be used by regional authorities and actors for good management of the field of quarrying to rationalize the national natural heritage.

Kristof De Bruyn, Robert Fleischer, Eleftheria Malami et al.

The phenomenon of $B^0_q$-$\bar{B}^0_q$ mixing ($q=d,s$) provides a sensitive probe for physics beyond the Standard Model. We have a careful look at the determination of the Unitarity Triangle apex, which is needed for the Standard Model predictions of the $B_q$ mixing parameters, and explore how much space for New Physics is left through the current data. We study the impact of tensions between inclusive and exclusive determinations of the CKM matrix elements $|V_{ub}|$ and $|V_{cb}|$, and focus on the $γ$ angle extraction. We present various future scenarios and discuss the application of these results for leptonic rare $B$ decays, which allows us to minimise the CKM parameter impact in the New Physics searches. Performing future projections, we explore and illustrate the impact of increased precision on key input quantities. It will be exciting to see how more precise data in the future high-precision era of flavour physics can lead to a much sharper picture.

Kuangdai Leng, Mallikarjun Shankar, Jeyan Thiyagalingam

Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w.r.t. coordinates of collocation points. In this paper, we present a novel and lightweight algorithm to conduct AD for physics-informed operator learning, which we call the trick of Zero Coordinate Shift (ZCS). Instead of making all sampled coordinates as leaf variables, ZCS introduces only one scalar-valued leaf variable for each spatial or temporal dimension, simplifying the wanted derivatives from "many-roots-many-leaves" to "one-root-many-leaves" whereby reverse-mode AD becomes directly utilisable. It has led to an outstanding performance leap by avoiding the duplication of the computational graph along the dimension of functions (physical parameters). ZCS is easy to implement with current deep learning libraries; our own implementation is achieved by extending the DeepXDE package. We carry out a comprehensive benchmark analysis and several case studies, training physics-informed DeepONets to solve partial differential equations (PDEs) without data. The results show that ZCS has persistently reduced GPU memory consumption and wall time for training by an order of magnitude, and such reduction factor scales with the number of functions. As a low-level optimisation technique, ZCS imposes no restrictions on data, physics (PDE) or network architecture and does not compromise training results from any aspect.