Quark-meson coupling model for a nucleon

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IMDS材料数据系统使用指南(10.0版本)

IMDS材料数据系统使用指南(10.0版本)
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1.4 1.5 1.6 127 日 2013 年 4 月 23 日
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1 IMDS – 简介.............................................................................................................................................................................................................. 7 2 IMDS – 使用入门 ...................................................................................................................................................................................................... 8
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量子多体系统的理论模型

量子多体系统的理论模型

量子多体系统的理论模型引言量子力学是描述微观物质行为的基本理论。

在量子力学中,描述一个系统的基本单位是量子态,而量子多体系统则是由多个量子态组成的系统。

由于量子多体系统的复杂性,需要借助一些理论模型来描述和研究。

本文将介绍一些常见的量子多体系统的理论模型,包括自旋链模型、玻色-爱因斯坦凝聚模型和费米气体模型等。

通过对这些模型的研究,我们可以深入了解量子多体系统的行为和性质。

自旋链模型自旋链模型是描述自旋之间相互作用的量子多体系统的模型。

在自旋链模型中,每个粒子可以处于自旋向上或向下的两种状态。

粒子之间通过自旋-自旋相互作用产生相互作用。

常见的自旋链模型包括Ising模型和Heisenberg模型。

Ising模型Ising模型是最简单的自旋链模型之一。

在一维Ising模型中,每个自旋可以取向上(+1)或向下(-1)。

自旋之间通过简单的相邻自旋相互作用来影响彼此的取向。

可以使用以下哈密顿量来描述一维Ising模型:$$H = -J\\sum_{i=1}^{N}s_is_{i+1}$$其中,J为相邻自旋之间的交换耦合常数,s i为第i个自旋的取向。

Heisenberg模型Heisenberg模型是描述自旋间相互作用的模型,与Ising模型不同的是,Heisenberg模型中的自旋可以沿任意方向取向。

常见的一维Heisenberg模型可以使用以下哈密顿量来描述:$$H = \\sum_{i=1}^{N} J\\mathbf{S}_i \\cdot \\mathbf{S}_{i+1}$$其中,$\\mathbf{S}_i$为第i个自旋的自旋算符,J为自旋间的交换耦合常数。

玻色-爱因斯坦凝聚模型玻色-爱因斯坦凝聚是一种量子多体系统的现象,它描述了玻色子统计的粒子在低温下向基态排列的行为。

玻色-爱因斯坦凝聚模型可以使用用薛定谔方程来描述:$$i\\hbar\\frac{\\partial}{\\partial t}\\Psi(\\mathbf{r},t) = -\\frac{\\hbar^2}{2m}\ abla^2\\Psi(\\mathbf{r},t) +V(\\mathbf{r})\\Psi(\\mathbf{r},t) +g|\\Psi(\\mathbf{r},t)|^2\\Psi(\\mathbf{r},t)$$其中,$\\Psi(\\mathbf{r},t)$是波函数,m是粒子的质量,$V(\\mathbf{r})$是外势场,g是粒子之间的相互作用常数。

多种群混沌映射麻雀优化算法

多种群混沌映射麻雀优化算法

多种群混沌映射麻雀优化算法下载温馨提示:该文档是我店铺精心编制而成,希望大家下载以后,能够帮助大家解决实际的问题。

文档下载后可定制随意修改,请根据实际需要进行相应的调整和使用,谢谢!并且,本店铺为大家提供各种各样类型的实用资料,如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等,如想了解不同资料格式和写法,敬请关注!Download tips: This document is carefully compiled by the editor. I hope that after you download them, they can help yousolve practical problems. The document can be customized and modified after downloading, please adjust and use it according to actual needs, thank you!In addition, our shop provides you with various types of practical materials, such as educational essays, diary appreciation, sentence excerpts, ancient poems, classic articles, topic composition, work summary, word parsing, copy excerpts,other materials and so on, want to know different data formats and writing methods, please pay attention!多种群混沌映射麻雀优化算法,是一种基于混沌映射和麻雀行为的智能优化算法,能够有效地应用于解决复杂的优化问题。

Search for a new state of matter – the Quark-Gluon Plasma一个物质–夸克胶子等离子体的新状态搜索 43

Search for a new state of matter – the Quark-Gluon Plasma一个物质–夸克胶子等离子体的新状态搜索 43
-- Fragmentation
-- multi-parton dynamics (recombination or coalescence or …)
-- Hydrodynamics (constituent quarks ? parton dynamics from gluons to constituent quarks? )
Nucleus-Nucleus Collisions and Volcanic Eruption
Volcanic high pT -- Strombolian eruption
Volcanic mediate pT – Spatter (clumps)
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Volcanic low pT – Bulk matter flows
Evidence for Multi-parton Dynamics in Hadronization of Bulk Partonic Matter at RHIC
Huan Zhong Huang (黄焕中) Department of Physics and Astronomy
University of California Los Angeles
Rafelski+Danos, Molnar+Voloshin …..) Quark Recombination – (R.J. Fries et al, R. Hwa et al)
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Constituent Quark Scaling
Constituent (n) Quark Scaling -- Meson n=2 and Baryon n=3 grouping
p td d td p N y d 2 1 π p td dtd p N 1 y i 12ic vo s ψ R () i)1( 0

Viscosities of Quark-Gluon Plasmas

Viscosities of Quark-Gluon Plasmas
Viscosities of Quark-Gluon Plasmas
H. Heiselberg
Nuclear Science Div., MS 70A-3307, Lawrence Berkeley Laboratory, Berkeley, CA 94720, USA The quark and gluon viscosities are calculated in quark-gluon plasmas to leading orders in the coupling constant by including screening. For weakly interaction QCD and QED plasmas dynamical screening of transverse interactions and Debye screening of longitudinal interactions controls the infrared divergences. For strongly interacting plasmas other screening mechanisms taken from lattice calculations are employed. By solving the Boltzmann equation for quarks and gluons including screening the viscosity is calculated to leading orders in the coupling constant. The leading logarithmic order is calculated exactly by a full variational treatment. The next to leading orders are found to be very important for sizable coupling constants as those relevant for the transport properties relevant for quark-gluon plasmas created in relativistic heavy ion collisions and the early universe.

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

纹理物体缺陷的视觉检测算法研究--优秀毕业论文

摘 要
在竞争激烈的工业自动化生产过程中,机器视觉对产品质量的把关起着举足 轻重的作用,机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检 测技术相比,自动化的视觉检测系统更加经济、快捷、高效与 安全。纹理物体在 工业生产中广泛存在,像用于半导体装配和封装底板和发光二极管,现代 化电子 系统中的印制电路板,以及纺织行业中的布匹和织物等都可认为是含有纹理特征 的物体。本论文主要致力于纹理物体的缺陷检测技术研究,为纹理物体的自动化 检测提供高效而可靠的检测算法。 纹理是描述图像内容的重要特征,纹理分析也已经被成功的应用与纹理分割 和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检 测算法。这种算法能容忍物体变形引起的图像配准误差,对纹理的影响也具有鲁 棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义,如缺陷区域 的大小、形状、亮度对比度及空间分布等。同时,在参考图像可行的情况下,本 算法可用于同质纹理物体和非同质纹理物体的检测,对非纹理物体 的检测也可取 得不错的效果。 在整个检测过程中,我们采用了可调控金字塔的纹理分析和重构技术。与传 统的小波纹理分析技术不同,我们在小波域中加入处理物体变形和纹理影响的容 忍度控制算法,来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字 塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段,我们检测了一系列 具有实际应用价值的图像。实验结果表明 本文提出的纹理物体缺陷检测算法具有 高效性和易于实现性。 关键字: 缺陷检测;纹理;物体变形;可调控金字塔;重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II

quorum冷冻传输工作原理

quorum冷冻传输工作原理

quorum冷冻传输工作原理quorum是一种基于以太坊区块链的企业级分布式账本平台。

在传输数据时,quorum利用了冷冻传输的工作原理,以确保数据的安全性和可靠性。

冷冻传输是一种在数据传输过程中使用冷冻技术保证数据的完整性和安全性的方法。

在quorum中,冷冻传输用于确保在数据从一个节点传输到另一个节点的过程中,数据不会被篡改或泄露。

quorum使用了加密技术来保护数据的机密性。

在数据传输之前,发送方使用加密算法对数据进行加密,然后将加密后的数据传输给接收方。

接收方收到数据后,使用相同的密钥和算法对数据进行解密,从而恢复原始数据。

这样,即使在传输过程中数据被截获,攻击者也无法获得有意义的信息,因为他们没有正确的密钥来解密数据。

quorum使用了数字签名技术来验证数据的完整性和真实性。

在数据传输之前,发送方使用自己的私钥对数据进行数字签名,然后将签名和数据一起传输给接收方。

接收方使用发送方的公钥来验证签名的有效性,以确保数据没有被篡改。

如果签名验证成功,接收方可以确定数据的完整性和真实性。

如果签名验证失败,接收方将拒绝接受数据或采取其他必要的措施来确保数据的安全性。

quorum还使用了分布式一致性算法来保证数据的可靠性。

在quorum 中,多个节点共同参与数据的传输和验证过程。

当一个节点发送数据时,其他节点将验证数据的正确性和完整性。

只有当大多数节点都验证通过时,数据才会被接受并存储到区块链中。

这种分布式一致性算法可以防止单点故障和数据篡改,提高数据的可靠性和安全性。

总结起来,quorum利用冷冻传输的工作原理来确保数据的安全性和可靠性。

通过加密技术保护数据的机密性,使用数字签名技术验证数据的完整性和真实性,以及应用分布式一致性算法保证数据的可靠性,quorum为企业级分布式账本平台提供了一个安全可靠的数据传输机制。

这使得quorum在金融、供应链和物联网等领域得到广泛应用,为企业提供了高效、安全和可信赖的数据交换和合作环境。

Geometric Modeling

Geometric Modeling

Geometric ModelingGeometric modeling is a fundamental concept in the field of computer graphics and design. It involves the creation and manipulation of digital representations of objects and environments using geometric shapes and mathematical equations. This process is essential for various applications, including animation, virtual reality, architectural design, and manufacturing. Geometric modeling plays a crucial role in bringing creative ideas to life and enabling the visualization of complex concepts. In this article, we will explore the significance of geometric modeling from multiple perspectives, including its technical aspects, creative potential, and real-world applications. From a technical standpoint, geometric modeling relies on mathematical principles to define and represent shapes, surfaces, and volumes in a digital environment. This involves the use of algorithms to generate and manipulate geometric data, enabling the creation of intricate and realistic 3D models. The precision and accuracy of geometric modeling are essential for engineering, scientific simulations, and industrial design. Engineers and designers utilize geometric modeling software to develop prototypes, analyze structural integrity, and simulate real-world scenarios. The ability to accurately model physical objects and phenomena in a virtual space is invaluable for testing and refining concepts before they are realized in the physical world. Beyond its technical applications, geometric modeling also offers immense creative potential. Artists and animators use geometric modeling tools to sculpt, texture, and animate characters and environments for films, video games, and virtual experiences. The ability to manipulate geometric primitives and sculpt organic forms empowers creatives to bring their imaginations to life in stunning detail. Geometric modeling software provides a canvas for artistic expression, enabling artists to explore new dimensions of creativity and visual storytelling. Whether it's crafting fantastical creatures or architecting futuristic cityscapes, geometric modeling serves as a medium for boundless creativity and artistic innovation. In the realm of real-world applications, geometric modeling has a profound impact on various industries and disciplines. In architecture and urban planning, geometric modeling software is used to design and visualize buildings, landscapes, and urban developments. This enables architects and urban designers toconceptualize and communicate their ideas effectively, leading to the creation of functional and aesthetically pleasing spaces. Furthermore, geometric modelingplays a critical role in medical imaging and scientific visualization, allowing researchers and practitioners to study complex anatomical structures and visualize scientific data in meaningful ways. The ability to create accurate and detailed representations of biological and physical phenomena contributes to advancementsin healthcare, research, and education. Moreover, geometric modeling is integral to the manufacturing process, where it is used for product design, prototyping,and production. By creating digital models of components and assemblies, engineers can assess the functionality and manufacturability of their designs, leading tothe development of high-quality and efficient products. Geometric modeling also facilitates the implementation of additive manufacturing technologies, such as 3D printing, by providing the digital blueprints for creating physical objects layer by layer. This convergence of digital modeling and manufacturing technologies is revolutionizing the production landscape and enabling rapid innovation across various industries. In conclusion, geometric modeling is a multifaceteddiscipline that intersects technology, creativity, and practicality. Its technical foundations in mathematics and algorithms underpin its applications in engineering, design, and scientific research. Simultaneously, it serves as a creative platform for artists and animators to realize their visions in virtual spaces. Moreover,its real-world applications extend to diverse fields such as architecture, medicine, and manufacturing, where it contributes to innovation and progress. The significance of geometric modeling lies in its ability to bridge the digital and physical worlds, facilitating the exploration, creation, and realization of ideas and concepts. As technology continues to advance, geometric modeling will undoubtedly play an increasingly pivotal role in shaping the future of design, visualization, and manufacturing.。

Quark transverse momentum in hard scattering processes

Quark transverse momentum in hard scattering processes

a r X i v :h e p -p h /9610288v 1 8 O c t 1996NIKHEF 96-024hep-ph/9610288Quark transverse momentum in hard scattering processes1R.Jakob 1,A.Kotzinian 2,3,4,P.J.Mulders(2π)4d 4ξe i p ·ξ P,S |(2π)4d 4ξe ik ·ξ 0|ψi (ξ)|P h ,X P h ,X |1Contributed paper at the 12th International Symposium on High Energy Spin Physics,Ams-terdam,Sept.10-14,1996The important extension when transverse momenta play a role,e.g.in processes in which there are at least two hadrons in addition to the virtual photon(or W/Z) such as1-particle inclusive lepton-hadron scattering(ℓH→ℓ′hX)or Drell-Yan (AB→µ+µ−X),is the presence of transverse separations in the nonlocal matrix ing constraints coming from hermiticity,parity and time reversal in-variance the most general parametrization of the Dirac projections,Φ[Γ](x,p T)=12(2π)3e ik·ξ P,S|M,(5)Φ[iσi+γ5]=S i T h1(x,p T)+λp i T2p2T g ij T S T jM Φ[γ+γ5]∂α(x)=SαT d2p T p2TP+SαT g T(x).(8)One has the relation g2(x)=(g T−g1)(x)=d g(1)1T/dx.The factor M/P+required by Lorentz invariance,leads to a factor M/Q in the cross sections.In order to obtain the cross section for a1-particle inclusive process the distri-bution part must be combined with a fragmentation part.At leading order for the case of summing over polarizations of thefinal state hadron one encounters e.g.∆[γ−](z)=1M z h cos(φh−φS)dσLTQ4λe|S T|y(2−y) a,¯a e2a x B g(1)a1T(x B)D a1(z h),(10)where the azimuthal angles are defined with respect to the lepton scattering plane.In this expression we have used the usual scaling variables,x B =Q 2/2P ·q ,y =P ·q/P ·k and z h =P ·P h /P ·q and we have included the summation over quark flavors and the weighting with the quark chargessquared.Figure 1:The function g (1)1T(x )ob-tained from the SLAC E143data and the WW parametrization.The relation between g 2and g (1)1T allows an estimate of the latter using the SLAC E143data [4]or the Wandzura-Wilczek (WW)part of g 2.This estimate is shown in Fig.1and would lead in e p →e ′π+X to an asymmetry proportional to g (1)u 1T /f u 1which is of the order of 0.05[5].A complete analysis of lepton-hadron scatter-ing can be found in ref.[6].E.g.the functionsh ⊥1L and h ⊥1T only appear in combination witha time-reversal odd fragmentation function H ⊥1[6,7].There are several theoretical aspects that have been stepsided here,such as the inclusion of diagrams dressed with gluons.In fact a wholetower of diagrams containing matrix elements with A +gluon fields in the target matrix ele-ment also contribute at leading order,precisely summing up to a gauge link needed to renderthe nonlocal matrix element Φcolor gauge invariant.Other gluon contributions are needed to ensure electromagnetic gauge invariance at order 1/Q or they lead to perturbative QCD corrections.Summarizing,we stress the fact that inclusion of transverse momenta of quarks in the formalism of nonlocal matrix elements extends the interpretability of structure functions in terms of quark distributions.The representation in terms of nonlocal quark fields also provides a natural link to models for estimating these functions.Part of this work (R.J.and P.M.)was supported by the foundation for Fun-damental Research on Matter (FOM)and the Dutch Organization for Scientific Research (NWO).1 D.E.Soper,Phys.Rev.D 15(1977)1141;Phys.Rev.Lett.43(1979)1847;J.C.Collins and D.E.Soper,Nucl.Phys.B194(1982)445;R.L.Jaffe,Nucl.Phys.B229(1983)2052J.P.Ralston and D.E.Soper,Nucl.Phys.B 152(1979)1093 A.P.Bukhvostov,E.A.Kuraev and L.N.Lipatov,Sov.Phys.JETP 60(1984)224E143collaboration,K.Abe et al.,Phys.Rev.Lett.76(1996)5875 A.Kotzinian and P.J.Mulders,Phys.Rev.D54(1996)12296P.J.Mulders and R.D.Tangerman,Nucl.Phys.B461(1996)1977R.Jakob and P.J.Mulders,contribution at SPIN96。

量子化学波谱计算基本流程

量子化学波谱计算基本流程

量子化学波谱计算基本流程英文回答:Quantum chemistry spectroscopy calculations involve several steps to determine the electronic structure and properties of molecules. The basic workflow typically includes the following steps:1. Geometry optimization: This step involves determining the most stable structure of the molecule by minimizing its potential energy. Various optimization algorithms, such as the gradient descent method, are used to find the equilibrium geometry.2. Basis set selection: A basis set is a set of mathematical functions used to approximate the wavefunction of the molecule. The choice of basis set affects the accuracy and computational cost of the calculations. Commonly used basis sets include the Gaussian basis set and the plane wave basis set.3. Electronic structure calculation: The electronic structure of the molecule is determined by solving theSchrödinger equation. This is typically done using methods such as Hartree-Fock theory, density functional theory (DFT), or post-Hartree-Fock methods like configuration interaction (CI) or coupled cluster (CC) theory. These methods provide information about the molecular orbitals, electronic energies, and properties.4. Spectroscopic property calculation: Once the electronic structure is determined, various spectroscopic properties can be calculated. For example, the vibrational frequencies can be obtained by solving the equations of motion for the nuclei using methods like harmonic or anharmonic vibrational analysis. The rotational spectrum can be calculated using methods like rigid rotor approximation or rotational-vibrational analysis.5. Comparison with experimental data: The calculated spectroscopic properties can be compared with experimental data to validate the accuracy of the calculations. Thishelps in understanding the molecular structure and dynamics, and also in predicting and interpreting experimental spectra.中文回答:量子化学波谱计算的基本流程包括以下几个步骤:1. 几何优化,通过最小化分子的势能来确定其最稳定的结构。

QuickCap NX商品说明书

QuickCap NX商品说明书

DATASHEET Overview QuickCap ® NX is a high-accuracy 3D parasitic field solver for foundry process technology development and circuit analysis. QuickCap NX includes key capabilities that address critical design challenges that occur in FinFET, nanosheet, and vertical FET process technologies down to 3nm. QuickCap NX is also used by foundries for modeling complex middle-of-line (MEOL) parasitic effects which have become more prominent at advanced process geometries. With its advanced process modeling features, a parallel execution mode and reference-level SPICE netlist generation and reduction capabilities, users can shorten the development cycle by more accurately predicting silicon performance.• Field solver solution for early process technology node exploration and parasitic modeling development • Advanced random-walk algorithm offers self-capacitance, coupling capacitance and distributed capacitance extraction for test structures and critical nets • Supports detailed process modeling of complex geometries and process effects for accurate analysis of device and interconnect parasitics • Used by Foundries for high-accuracy 3D modeling using uniquely detailed silicon profiles • 3D graphical viewer allows visibility into the exact process profile being modeled • Faster runtime enabled by multicore processing, tiling, bounded nets andhierarchical extraction for increased designer productivitySPEFQuickCap NX Tech File Tech File Figure 1: QuickCap NX 3D field solver solution enables early process exploration and characterizationQuickCap NX is thegold standard 3DField Solver solutionfor advancedprocess technologiesdown to 3nm withFinFET , nanosheet,and vertical FETdevice architecturesQuickCap NX 3D Field SolverAs geometries shrink and clock frequencies increase, designers need more accurate parasitic values to reduce risk of design failure. The growing need for more accuracy makes it necessary to account for precise fringing electrostatic fields and process effects in test structures and analyzing critical cells, blocks, and nets. QuickCap NX provides robust, consistent, and accurate 3D capacitance extraction with capacity to handle moderately sized blocks or long critical nets. QuickCap NX’s proven modeling capabilities allow users to perform accurate noise and timing analysis for robust design development and improved silicon success.Advanced 3D Modeling and Process DevelopmentQuickCap NX includes the ability to create 3-D physical models which precisely match advanced process technology profiles and account for new effects like, diamond EPI growth, EPI and trench contact parasitics, and non-linear gate resistance. The unique capability allows foundries and early technology adopters to engage earlier to explore device parasitic capacitance effects in new technology nodes and accelerate the development schedules.An exclusive technology file encryption feature provides foundries with a secure method of sharing critical process information with their customers, allowing them to enhance the accuracy of their analysis and speedup the migration to new nodes. In addition, multicore capabilities and hierarchical processing significantly improve runtime, and a powerful 3D graphics viewer simplifies the development and debug of new complex circuit structures and technology files. As a result, QuickCap NX is broadly used in process studies, characterization and correlation across several generations of process nodes, and to support highly accurate device-level SPICE simulations.Modeling down to 3nmFinFET, nanosheet, and vertical FET process technologies achieve better control over the source-drain channel because the gate encloses the channel on three or more sides, resulting in higher mobility, greater drive strength, lower switching currents, and lower leakage currents. This 3D architecture also introduces more complex geometries and many new capacitive elements that require highly accurate modeling. QuickCap NX’s geometry pre-processing engine provides a highly accurate physical profile of the FinFET, nanosheet, or vertical FET for modeling, and its graphical viewer allows users to see exactly how the device will be modeled. Finally, the core 3-D field-solver engine of QuickCap NX accurately extracts the capacitance values from the model. Combined, thesecapabilities allow QuickCap NX to provide the necessary precision to model devices down to 3nm and as a result it has been adopted by leading foundries for this purpose.Powerful Geometry Processing EngineThe geometry pre-processor capability “gds2cap” translates the 2D layout data into a 3D representation and reduced SPICE netlist with resistance and capacitance. The gds2cap capability includes a flexible polygon-processing engine that handles multipleconformal dielectrics, non-Manhattan geometries, non-planar metals, metal fill, process effects (OPC, CMP , Trapezoidal wire), device recognition, resistance extraction and exclusion of device capacitances. QuickCap NX takes the 3D representation and the netlist output from the gds2cap interface, produces an output file containing self and coupling- capacitance values and replaces thecapacitance values in the netlist with accurately computed values from QuickCap NX.Detailed Silicon Profile based 3D Modeling Accurate Middle-of-line Parasitic Extraction Gate Fin Source DrainM0V0M0V0 Figure 2: QuickCap NX is used by foundries for FinFET modeling down to 5nmAs drawn OPC width = f(w,s)Sidewall shape = f(z)Width expanded = f (layer)Sidewall OPC = F(w,s,z)Thickness = f (local density)Figure 3: QuickCap NX provides precise physical models of process effectsProven Parasitic Capacitance ExtractionQuickCap NX has demonstrated close correlation to silicon measurements at various process nodes. Its advanced modeling,including modeling of optical, copper, Ruthenium, and new conducting material effects as well as in-die process variations, enable increased accuracy. The validation of QuickCap NX’s silicon accuracy by foundries has led to its wide use in early process technology development and device characterization.For advanced users, QuickCap NX also provides a dial-in accuracy and error- bounds reporting on each net, providing the flexibility and control for their target application needs.Handling Large LayoutsQuickCap NX provides multiple techniques to enable critical net analysis in designs too large to fit in memory. Runtime or memory use can be reduced by using tiling, bounded nets, hierarchical processing, multicore processing or a combination of these techniques.Key Features Summary• 3D modeling of FinFET, nanosheet, and vertical FET process technologies down to 3nm• Accurate extraction of self-coupling and distributed capacitance• Robust and accurate handling of complex geometries including non-Manhattan structures, conformal dielectrics,and floating metal• Advanced process effects for in-dieprocess variation, optical, copper, Ruthenium, and new conducting material effects• 3D graphics viewer• Dial-in accuracy and error bounds reporting for each net• Low memory usage independent of accuracy• Runtime independent of net length• Exclusion of device capacitance and optional inclusion of device fringe capacitance• Parasitic reduction• Flat and hierarchical processing• Tile or bounded net analysis• Multicore processing• Technology file encryptionInput• GDSII or scripted text• Output• Back annotated SPICE netlist• Capacitance summary in a matrix• Platform/OS• 64 bit Solaris and LinuxFor more information about Synopsys products, support services or training, visit us on the web at: , contact your local sales representative or call 650.584.5000.©2018 Synopsys, Inc. All rights reserved. Synopsys is a trademark of Synopsys, Inc. in the United States and other countries. A list of Synopsys trademarks isavailable at /copyright.html . All other names mentioned herein are trademarks or registered trademarks of their respective owners.。

一个具有三代家族的夸克与轻子的复合模型

一个具有三代家族的夸克与轻子的复合模型

一个具有三代家族的夸克与轻子的复合模型夸克与轻子复合模型是指由夸克和轻子组成的基本粒子模型。

夸克是构成质子和中子等强子的基本粒子,而轻子包括电子、中微子等基本粒子。

这个模型的提出是为了解释强子和轻子之间的关系,并为科学家提供更全面的理解物质构成的基础。

夸克与轻子复合模型的第一代主要包括六个基本粒子:上夸克(up quark)、下夸克(down quark)、电子(electron)、电子中微子(electron neutrino)、正电子(positron)和正电子中微子(positron neutrino)。

这些粒子的特点是具有电荷、质量和自旋等物理性质。

上夸克和下夸克是构成质子和中子的基本粒子,它们具有不同的电荷和质量。

在这个模型中,质子由两个上夸克和一个下夸克组成,而中子则由两个下夸克和一个上夸克组成。

质子和中子是构成原子核的重要组成部分,它们稳定的结构对于整个宇宙的稳定性至关重要。

电子是原子中最轻的带电基本粒子,它在原子中负责电荷平衡。

电子中微子是一种质量非常小的轻子,具有特殊的能量转移特性。

正电子是电子的反粒子,具有正电荷。

第二代夸克与轻子复合模型增加了两个新的夸克:奇夸克(strange quark)和魅夸克(charm quark),以及两个新的轻子:缪子(muon)和缪子中微子(muon neutrino)。

这些粒子与第一代的粒子相似,但具有不同的质量和能量转移特性。

奇夸克和魅夸克在质量和电荷等方面与上、下夸克有所不同,它们与其他夸克和轻子之间可以发生相互转化的过程,这种转化过程是粒子物理学中重要的研究课题之一。

缪子和缪子中微子是电子和电子中微子的变体,它们具有与电子和电子中微子相似的物理性质,但质量比它们大。

第三代夸克与轻子复合模型增加了两个新的夸克:顶夸克(top quark)和底夸克(bottom quark),以及两个新的轻子:τ子(tau)和τ子中微子(tau neutrino)。

夸克物质界面张力QCD计算

夸克物质界面张力QCD计算

夸克物质界面张力QCD计算夸克物质的研究一直是粒子物理学的一个重要方向,而夸克物质界面的张力计算则是其中的一个关键问题。

在本文中,我们将介绍用量子色动力学(QCD)来计算夸克物质界面张力的方法和相关理论。

1. 引言夸克是构成强子的基本粒子之一,而在夸克物质中,夸克之间的强相互作用会导致夸克之间的相互作用力。

夸克物质界面的张力即为夸克之间相互作用力的表现。

2. QCD的基本原理量子色动力学是夸克与胶子相互作用的理论框架,描述了强子相互作用的基本规律。

在QCD中,夸克通过交换胶子来相互作用,而胶子则负责传递强相互作用力。

3. 夸克物质界面的定义夸克物质界面是指夸克之间的相互作用区域,当两个夸克靠近时,由于强相互作用力的存在,夸克之间会形成一个张力场,即界面张力。

4. 界面张力的计算方法界面张力的计算需要借助QCD的计算方法。

在QCD中,可以通过路径积分的方法来计算夸克物质的各种性质。

对于界面张力的计算,可以采用格点规范场论的方法,在离散化的空间上进行计算。

通过计算路径积分,可以得到夸克物质界面的张力。

5. 数值模拟与实验验证为了验证理论计算的准确性,需要进行数值模拟和实验验证。

在数值模拟中,可以采用蒙特卡洛方法对夸克物质界面的张力进行模拟计算。

而实验验证则需要通过高能物理实验来测量夸克物质界面的性质,并与理论计算结果进行对比。

6. 应用与展望夸克物质界面张力的计算在核物质、宇宙学等领域都有重要应用。

通过精确计算夸克物质界面的张力,可以推进我们对强子相互作用的理解,并为相关科学研究提供更精确的理论依据。

未来,随着技术的进步,夸克物质界面张力的计算方法也会不断完善,并为更多领域的研究提供支持。

结论夸克物质界面张力的计算是粒子物理学中的一个重要课题,借助量子色动力学的理论和计算方法,我们可以对夸克物质界面的张力进行准确计算。

这对于推进强子相互作用的研究以及相关领域的应用都具有重要意义。

通过本文的介绍,希望能够对夸克物质界面张力的QCD计算方法有一个初步的了解,并为相关研究提供一定的参考。

夸克物质的QCD相图第一性原理计算

夸克物质的QCD相图第一性原理计算

夸克物质的QCD相图第一性原理计算夸克物质是一种存在于高能物理领域中的基本粒子构成的物质,其性质在我们理解宇宙演化和核物理中起着至关重要的作用。

夸克胶子理论(Quantum Chromodynamics,简称QCD)是描述夸克之间相互作用的一种理论框架,它是标准模型的一部分。

在研究夸克物质时,研究者们往往需要借助QCD相图进行分析。

QCD相图是用来描述夸克物质在不同条件下可能出现的相态的图像,可以帮助我们了解夸克物质的行为和性质变化。

然而,QCD相图的计算十分复杂,传统方法并不能满足精确计算的需求。

为了解决这一问题,科学家们采用了第一性原理计算的方法。

第一性原理计算是指通过基本原理和数学方程来推算出系统的性质,而无需过多依赖经验或实验数据。

在夸克物质的QCD相图研究中,第一性原理计算主要基于重整化群和格点规范理论。

首先,重整化群理论是理解QCD等场论中不同能量尺度下的行为的重要工具。

它描述了类似于放大镜的效应,即在不同尺度下,系统的行为表现出不同的性质。

通过重整化群理论,研究者们可以对夸克物质的行为进行不同尺度下的推演,从而揭示出其中的规律和特性。

其次,格点规范理论是一种在离散空间网格上对场论进行计算的方法,尤其适用于高能物理中的夸克胶子理论。

格点规范理论对夸克物质的QCD相图的计算提供了重要的数学工具和框架,能够帮助我们对夸克物质的相态和相互作用进行精确的模拟和计算。

通过基于第一性原理的计算方法,研究者们能够在计算机上模拟夸克物质在各种温度、密度、化学势等条件下的行为,并得到相应的QCD相图。

这些结果不仅可以提供对夸克物质性质的深入理解,还对理论物理的发展和实验研究提供了重要参考。

总结起来,夸克物质的QCD相图第一性原理计算是一种重要的研究方法,通过重整化群和格点规范理论的运用,科学家们能够对夸克物质的相态和相互作用进行精确模拟和计算,从而揭示其性质和行为的奥秘。

这一计算方法在高能物理研究中具有重要意义,对于我们理解夸克物质和宇宙演化的规律具有重要价值。

夸克物质表面张力QCD第一性原理计算

夸克物质表面张力QCD第一性原理计算

夸克物质表面张力QCD第一性原理计算表面张力是描述液体表面分子间相互作用的物理量,它的大小决定了液体的表面性质和表面现象。

在传统的物质中,表面张力的计算往往基于分子之间的相互作用和经验参数。

然而,在极端条件下的物质中,如夸克物质,传统的计算方法并不再适用。

为了更准确地计算夸克物质的表面张力,我们可以借助量子色动力学(QCD)的第一性原理计算方法。

夸克物质是构成核物质的基本粒子,它们具有强相互作用和色电荷。

QCD是描述强相互作用的理论,它使用了夸克场和胶子场来描述核力的作用机制。

在夸克物质的表面,夸克和胶子的相互作用对表面张力的贡献将十分重要。

首先,我们需要使用QCD的拉格朗日密度来描述夸克和胶子的作用。

利用这个密度可以得到QCD的方程组,然后我们可以使用数值方法对这个方程组进行求解。

在计算中,我们需要采用适当的边界条件来模拟夸克物质的表面。

这些边界条件可以是夸克场和胶子场在表面处取特定的数值或者满足特定的波函数形式。

接下来,我们可以使用拉格朗日密度计算夸克和胶子的场方程,并根据边界条件求解这些方程。

通过这样的计算,我们可以得到夸克物质的场分布和能量密度。

为了计算表面张力,我们需要进一步使用能量-动量张量计算物质的能量和动量。

根据能量-动量张量,表面张力可以通过能量密度在表面上的变化来确定。

在QCD计算中,我们可以通过比较夸克物质的能量密度在表面和体积内的差异,来得到表面张力的估计。

当然,由于QCD是一种非常复杂的理论,对于夸克物质的表面张力计算可能需要使用高性能计算机和大规模的数值模拟。

这是因为夸克物质的表面和体积的相互作用非常复杂,需要考虑大量的夸克和胶子的相互作用。

然而,通过QCD的第一性原理计算,我们可以得到更加准确的夸克物质表面张力的结果,从而更好地理解夸克物质的性质和行为。

综上所述,夸克物质表面张力的计算需要借助量子色动力学的第一性原理计算方法。

通过使用QCD的方程组和拉格朗日密度,我们可以计算夸克和胶子在表面处的场分布和能量密度。

什么是量子近似优化算法

什么是量子近似优化算法

什么是量子近似优化算法
量子近似优化算法(Quantum Approximate Optimization Algorithm,简称QAOA)是一种经典和量子的混合算法,是一种在基于门的量子计算机上求解组合优化问题的变分方法。

QAOA是一种分层结构的量子优化算法,它将优化问题分解成多个子问题,使用量子计算机来解决这些子问题。

它的基本原理是,将优化问题转化为一系列量子约束,然后利用量子计算机的一系列控制操作来搜索解决优化问题的最优解。

QAOA的优势在于,它能够在更短的时间内解决复杂的优化问题,而且它可以求解多种形式的优化问题,包括二次规划、非线性规划等。

它的算法结构也比传统优化算法更简单,更容易实现。

QAOA还可以用于解决实际问题,比如路径规划、调度优化等,它可以有效提高优化问题的求解精度和速度。

QAOA是一种先进的量子优化算法,它可以有效求解各种优化问题,提高优化效率,为实际应用提供更好的解决方案。

Improved Quark-Meson Model

Improved Quark-Meson Model

Improved Quark-Meson Model
Li Xiguo;Guo Yanrui;Liu Ziyu;Jin Genming
【期刊名称】《近代物理研究所和兰州重离子加速器实验室年报:英文版》【年(卷),期】2005(000)001
【摘要】Since the conjecture of Witten that strange quark matter (SQM) would be more stable than the normal nuclear matterin is got, much theoretical effort has been directed toward the investigation of its properties and applications.Many effective models reflecting the characteristics of the strong interaction are used to study the SQM. They include the MIT bag model,the quark meson coupling(QMO) model,【总页数】2页(P20-21)
【关键词】奇异夸克物质;子模型;麻省理工学院;相互作用;袋模型;平方;介子
【作者】Li Xiguo;Guo Yanrui;Liu Ziyu;Jin Genming
【作者单位】不详
【正文语种】中文
【中图分类】O572.33
因版权原因,仅展示原文概要,查看原文内容请购买。

夸克物质界面张力QCD第一原理计算

夸克物质界面张力QCD第一原理计算

夸克物质界面张力QCD第一原理计算夸克物质界面张力:QCD第一原理计算在物理学中,夸克是构成一切物质的基本粒子之一。

夸克物质的界面张力是一个重要的物性量,描述了夸克物质中不同相之间的相互作用力。

界面张力的计算对于理解夸克物质中的相变、相分离以及相互作用的本质起着至关重要的作用。

本文将基于QCD(量子色动力学)的第一原理计算方法,探讨夸克物质界面张力的计算方法和相关研究进展。

一、QCD的基本原理和夸克物质界面张力量子色动力学(Quantum Chromodynamics,QCD)是一种研究强相互作用的基本理论,描述了夸克和胶子之间的相互作用。

在夸克物质中,夸克与胶子形成夸克胶子等离子体,其界面张力成为了研究的重点之一。

界面张力是指两个相之间的边界上单位面积所受的张力。

在夸克物质中,界面张力的大小决定了夸克胶子等离子体中不同相之间的分离程度。

QCD提供了计算夸克物质界面张力的理论框架,其计算方法基于拉格朗日量和路径积分的形式。

二、夸克物质界面张力的计算方法夸克物质界面张力的计算方法主要包括拉格朗日量的构建和路径积分的计算。

首先,需要构建描述夸克胶子等离子体的拉格朗日量。

其次,利用路径积分的方法,采用蒙特卡洛模拟等数值计算技术,计算出夸克物质界面张力。

在构建拉格朗日量时,需要考虑到夸克和胶子之间的作用,以及相变过程中的变化。

QCD提供了描述夸克和胶子相互作用的理论框架,可以通过有效相互作用有效子理论(Effective Field Theory, EFT)来描述夸克物质的低能态。

通过引入一些合适的参数和近似方法,可以确定夸克物质的界面张力。

路径积分作为计算夸克物质界面张力的重要工具,将所有可能的路径纳入计算过程中,得到了系统的基态波函数。

利用蒙特卡洛模拟等数值计算技术,可以计算出夸克物质界面张力的数值解。

这种计算方法不仅可以获得夸克物质界面张力的数值,还可以进一步研究夸克物质的相变和相分离。

三、夸克物质界面张力的研究进展随着计算方法和技术的发展,夸克物质界面张力的研究也取得了一系列重要进展。

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a r X i v :n u c l -t h /0010067v 1 20 O c t 2000Quark-Meson Coupling Model for a NucleonS.W.Hong †,B.K.Jennings ∗†Department of Physics and Institute of Basic Science,Sungkyunkwan University,Suwon 440-746,Korea ∗TRIUMF,4004Wesbrook Mall,Vancouver,British Columbia,Canada V6T 2A3ABSTRACTWe considered the quark-meson coupling model for a nucleon.The model describes a nucleon as an MIT bag,in which quarks are coupled to the scalar and the vector mesons.A set of coupled equations for the quark and the meson fields are obtained and are solved in a self-consistent manner.We show that the mass of a dressed MIT bag interacting with σ-and ω-meson fields differs considerably from the mass of the free MIT bag.The effects of the density-dependent bag constant are investigated.The results of our calculations imply that the self-energy of the bag in the quark-meson coupling model is significant and needs to be considered in doing the calculations for nuclear matter or finite nuclei.1IntroductionFor more than a decade the success of quantum hadrodynamics 1has been rather impressive in describing the bulk properties of nuclear matter as well as the properties of finite nuclei.The model is rather simple with only a few parameters,and yet it has been successfully applied to a great number of problems for nuclear matter and nuclear structure.In this model the relevant degrees of freedom are nucleons and mesons,but the nucleons having a com-posite structure are treated as Dirac particles.Several years ago a model to remedy this problem was proposed by Guichon 2.He proposed a quark-meson coupling (QMC)model,in which quarks and mesons are explicitly dealt with.The model describes nuclear matter as non-overlapping,static,spherical MIT bags 3,4interacting through the self-consistent exchange of scalar (σ)and vector (ω)mesons in the mean-field ing this model,he investigated the direct quark degrees of freedom in nuclear matter.The model was refined1later to include the nucleon Fermi motion and the center-of-mass corrections to the bag energy5and was applied to a variety of problems[6-10].In the MIT bag model3,4the bag constant B and a phenomenological parameter Z arefixed such that the nucleon mass of939MeV is reproduced for some bag radius R.The bag constant B produces the pressure to make a bubble in the QCD vacuum.Z is to account for various corrections including the zero-point motion.In the QMC model for nuclear matter the quark-σcoupling constant(g qσ)and the quark-ωcoupling constant(g qω)are additionally ually,B and Z arefixedfirst so that the mass of the free MIT bag becomes equivalent to the nucleon mass of939MeV for a certain bag radius.We denote by B free and Z free,respectively,the bag constant B and the parameter Z for the free MIT bag.B free and Z free thus obtained are used in the QMC model calculations,and then g qσand g qωare determined to reproduce the binding energy per nucleon(B.E.=−16MeV)at the saturation density(ρ0N=0.17fm−3).However,due to the interaction between the quarks and theσ-and theω-mesons the mass of a single dressed MIT bag in free space,when calculated with B free and Z free described above,may be different from the nucleon mass of 939MeV.Such a possible deviation of the mass of a dressed bag from the value of939MeV has been neglected in previous QMC model calculations for nuclear matter.If the deviation in mass is significant,it is necessary to modify the parameters B free and Z free before implementing them in the nuclear matter calculations.In this paper we investigate this change in the nucleon mass.In Section2the QMC model for a nucleon is described.Some numerical results are presented in Section3.Section4contains a summary.2Quark-meson coupling model for a nucleonThe Lagrangian density for the MIT bag in which quarkfieldsψare coupled to theσ-andω-fields may be written asL=i2¯ψψ∆s−12m2ωωµωµ+1bag constant B(σ)asBδσ2(1−iγµnµ)ψ∆s,(3)where∆s=−n·∂(θv)is used.The left hand side of Eq.(3)gives us the equation for the quarks inside the bag,and the right hand side gives us the linear boundary condition at the bag surface.The equations forσandωµare, respectively,∂µ∂µσ+m2σσ= g qσ¯ψψ−∂B(σ)dr=− 2f(r)dr=E+(r)f(r),(7) where g(r)and f(r)are the radial parts of the upper and the lower componentsofψ,i.e.,ψ(t,r)=e−iǫq t/R g(r)−i σ·ˆr f(r) χq4π,(8)where σis the Pauli spin matrix andχq is the quark spinor.Also,E+(r)=ǫqR−g qωω0(r)−(m q−g qσσ(r))(9)3withσ(r)andω0(r)being theσ-and the time component of theω-fields.For the ground state Eqs.(9)are the equations in the radial coordinate r only.The linear boundary condition from the right hand side of Eq.(3),when rewritten by using Eq.(8),becomesf xr R (10) at the bag surface,r=R,and determines the eigenvalue x of the quarks.ǫq is then given by∂σ−g qσ(3ρs)θ(R−r)(11)and(∇2−m2ω)ω0(r)=−g qω(3ρq)θ(R−r),(12) respectively,whereρs(=¯ψψ)is the scalar density and3is explicitly multiplied by the densities(ρs andρq)here to account for the sum over3quarks.Equa-tions(11)and(12),which are nothing but the Klein-Gordon equations with source terms,may be readily solved using the Green’s function defined by the equation(∇2−m2i)G i(r,r′)=δ(r−r′)(i=σ,ω).(13) For the s-wave state G i(r,r′)may be written as11G i(r,r′)=−1m i rr′sinh(m i r<)e−m i r>.(14)Equations(6),(7),(11),and(12)form a set of coupled equations forψ,σ,and ω0,which need to be solved self-consistently.By solving these equations we can obtain the eigenvalue of the quarks and the energy of the nucleon bag.One way to calculate the energy(E N)of the nucleon is computing the energy-momentum tensor Tµνand using the relationE N= d3r T00.(15) T00may be written asT00= E q2 (∇ω0)2+(mωω0)2 +1where E q is given byE qR−ZR−ZE2N− p2c.m. ,(18) where p2c.m. = 3k=1 p2k =3(x/R)2.By calculating M N for each bag radius and minimizing M N with respect to the bag radius R,we can get the nucleon mass and the bag radius.3ResultsWefirst present some results of our calculations of the nucleon mass when we take the model parameters from Ref.[10].There are several sets of pa-rameters used in Ref.[10].Here we only consider the cases when R=0.6 fm andδ=8.In Fig.1M N is plotted as a function of the bag radius R.The solid curve represents the free MIT bag mass M freeNwithout a couplingof the quarks with the mesons;g qσ=g qω=g Bσ=0.B1/40and Z are takento be188.1MeV and2.0306,10,respectively,which produce the minimum ofM freeN at939MeV and R=0.6fm.Now we include the couplings betweenthe quarks and the mesons.We take the parameter sets used to plot Fig.1 of Ref.[10].When g qσ=0andδ=8,we have g Bσ=8.45and g qω=3.00 from Table1of Ref.[10].M N obtained with these parameters are plotted by the dashed curve in Fig. 1.M N is reduced to848MeV with R=0.694 fm.When g qσ=1.0andδ=8,and thus g Bσ=7.26and g qω=3.08from Table1of Ref.[10],we get M N as plotted by the dotted curve in Fig.1with M N=834MeV at R=0.674fm.Let us consider the case when g qσ=2.0 andδ=8.Then from Table1of Ref.[10]we have g Bσ=5.65and g qω=2.85. M N obtained with these parameters are plotted by the dash-dotted curve in Fig.1with M N=830MeV and R=0.659fm.Fig.1of Ref.[10]shows that for these three different sets of parameters the nuclear matter binding energies calculated by the QMC model are the same particularly near the saturation density.However,the present calculations show that the nucleon masses cal-culated with these parameter sets are considerably different from each other,all being far from M freeN =939MeV.The couplings induce large attractionand reduce the nucleon mass by about100MeV.Fig.1also shows that the attraction is not uniform with respect to the bag radius.There is more attrac-tion at larger bag radii,and as a result the bag radius increases significantly.5for different coupling for all calculations.curve,g qσ=1, and g Bσ=5.65.cases.Let’suse B1/40=188.1in Fig.2byquarks and theconstant,i.e.,us M N=957g qσ=2andis induced and wecurve.Whenthe attractioncurve, Now we consider the density-dependency in the bag constant using Eq.(2).We start with the case that g qσ=g qω=2and g Bσ=0,corresponding to the dash-dotted curve in Fig.2.This curve is plotted again in Fig.3by thedash-dotted curve.(M freeN is also plotted in Fig.3by the solid curve.)To seethe effects of the density-dependency of the bag constant,we take g Bσ=1, keeping g qσ=g qω= 2.The calculated M N is represented by the dashed curve.Reducing the bag constant by introducing the density-dependency6for different coupling for all calculations.g qσ=2and g qω=0.here g Bσ=0arecoupling constantis induced, from Eqs.(11) increases(nega-in enhancedfield for R=0.6fm as a function of the radial coordinate r.The dotted,dashed,and dash-dotted curves in Fig.4are obtained with the corresponding parameters used for the dotted,dashed,and dash-dotted curves in Fig.3,respectively.The figure shows that as g Bσbecomes larger theσfield increases due to the in-mass for the largersmaller mass,too.the dotted,dashed,curves,respec-only the nucleonWhen the bagessentiallyfixed asthe nucleon masswe introduceincreases as shownthe M N curveat larger bagsource functionand−g qσ3ρsDue toradii.Therefore,iffor large radii.0.8fm.σ(r)forregion,which is the significant radial region.As a result,there is more attraction at large bag radii.8−g qσ3ρs are plotted by used are g qσ=g qω=2solid and the dashed used are g qσ=g qω=20.6to1.0fm and the bag mass dueSuch self-energyinto account inchoosing the parameters for the calculation of nuclear matter properties.The density-dependent bag constant can not only change the nucleon mass but also shift the bag radius.4SummaryWe have applied the QMC model to a single nucleon.Recently the model has been often used to investigate explicit quark degrees of freedom in describing the nuclear matter.However,in the previous calculations the change in the mass of the bag due to the self-energy was ignored.Our calculations suggest that this change in the nucleon mass is not negligible,and thus the model parameters need to be modified to take this effect into consideration when they are used in nuclear matter calculations.5AcknowledgementThis work was supported by the Natural Sciences and Engineering Research Council of Canada.SWH acknowledges kind hospitality at TRIUMF and was partially supported by Sungkyun Faculty Research Fund and KOSEF Korea-Japan International Collaboration Program(986-0200-003-2).1.B.D.Serot and J.D.Walecka,Adv.Nucl.Phys.16,1(1986).2.P.A.M.Guichon,Phys.Lett.B200,235(1988).3.A.Chodos,R.L.Jaffe,K.Johnson,C.B.Thorn,and V.Weisskopf,Phys.Rev.D9,3471(1974);A.Chodos,R.L.Jaffe,K.Johnson,andC.B.Thorn,Phys.Rev.D10,2599(1974).4.A.W.Thomas,Adv.Nucl.Phys.13,1(1984).5.S.Fleck,W.Bentz,K.Shimizu,and K.Yazaki,Nucl.Phys.A510,731(1990).6.K.Saito and A.W.Thomas,Phys.Lett.B3279(1994).7.K.Saito and A.W.Thomas,Phys.Lett.B33517(1994);363157(1995);Phys.Rev.C512757(1995);522789(1995).8.A.W.Thomas,A.Michels,A.W.Schreiber,and P.A.M.Guichon,Phys.Lett.B23343(1989);K.Saito,A.Michels,and A.W.Thomas, Phys.Rev.C46R2149(1992);K.Saito and A.W.Thomas,Nucl.Phys.A574659(1994).9.H.Q.Song and R.K.Su,Phys.Lett.B358179(1995).10.X.Jin and B.K.Jennings,Phys.Lett.B37413(1996);Phys.Rev.C541427(1996).11.J.D.Jackson,Classical Electrodynamics,3rd.,(John Wiley&Sons,Inc.,New York,1999)pp.427.10。

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