在BES实验中用神经元网络法鉴别e,μ,π

研究了两种不同的神经元网络法,BP网络和LVQ网络,对北京谱仪(BES)实验中e,μ,π粒子的鉴别,取得了较常规方法要好的结果.用于训练和检验的μ子样本来自宇宙线事例,e和π粒子则是由真实实验数据精选的,虽然样本本身具有非均匀的动量谱,但BP网络的检验结果给出的粒子选择效率在整个动量区间却仍然具有相当均匀的分布,LVQ网络稍逊之.至少是在我们研究的这一课题中证明了BP网络的模式识别功能要优于LVQ网络.     

北京谱仪（BESⅡ）上p和p^－鉴别的研究

以北京谱仪(BES)上常用的3种粒子鉴别方法为基础，充分利用BESⅡ所获取的R值扫描、J／ψ和ψ’数据中p，p^-以及其他带电粒子样本，对动量范围在0．3-1．2GeV的p和p鉴别方法进行分析研究．找到BESⅡ上p和p^-的最佳鉴别方法：动量低于0．6GeV只用dE／dx实现粒子鉴别，动量高于0．6GeV可用TOF或联合鉴别的方法，并给出p，p^-的鉴别效率以及本底的混入比例．     

利用TOF的Q值进行粒子鉴别的研究

飞行时间计数器(TOF)是北京谱仪(BESⅡ)中用于粒子鉴别的重要子探测器. 当带电粒子击中TOF时，闪烁计数器同时测量粒子的飞行时间T和脉冲幅度Q. 本文利用BESⅡ采集的双m事例，对脉冲幅度Q进行了研究. 利用J/ψ强子事例样本，给出了脉冲幅度最可几值Qmp随粒子bg值的变化关系. 这表明可以利用TOF测量的脉冲幅度Q值实现对粒子的鉴别. 最后给出用脉冲幅度Q进行粒子鉴别的效率和误判率.     

BESⅢ探测器粒子鉴别效率的蒙特卡罗研究

利用ψ（3770）DD衰变的运动学特性,通过分析工作在BEPC-Ⅱ上BESⅢ探测器在正负电子对撞质心系能量为3.773GeV处的e＋ e_ψ（3770）蒙特卡罗模拟事例,挑选出干净的介子和K介子样本,研究了BESⅢ粒子识别系统对带电粒子的鉴别效率。根据此方法,可以确定数据和蒙特卡罗样本在粒子鉴别效率方面的差异。     

粒子鉴别中dE/dx和TOF的权重

在北京谱仪数据分析中,粒子鉴别使用了dE/dx和TOF的信息.在利用dE/dx和TOF联合鉴别粒子时,通常的做法是把两者的χ²值等权重相加(χ²方法).在不同的动量区间,dE/dx和TOF对粒子的鉴别能力不同.本文给出两者联合鉴别时不同动量区间的权重,并构造一个新的线性变量来进行粒子鉴别     

CsI(Tl)闪烁探测器用于粒子鉴别

根据CsI(Tl)晶体产生的闪烁光脉冲具有快、慢两种成份这一特性,采用电荷灵敏ADC对快、慢成份分别积分的方法,仅用一块CsI(Tl)晶体实现了对轻粒子p、d、α和Li的鉴别与测量,且得到了好的粒子分辨本领.另外,用CsI(Tl)晶体与Si半导体组成的ΔE-E望远镜也很好地实现了对轻粒子的鉴别.     

新一代北京谱仪的设计与建造

 北京谱仪（BESIII）作为北京正负电子对撞机重大改造工程的一个重要组成部分，是北京正负电子对撞机的“眼睛”，它通过测量正负电子对撞产生的次级粒子来研究物质的基本组成及其性质。本文介绍我们自行设计与建造的BESIII探测器的设计与研制情况。     

JLab Hall-A上小角度GDH实验中的粒子鉴别及其效率

在JLab的A大厅上的小角度GDH实验中, 因为散射截面及截面不对称度的测量需要干净的电子样本和足够的事例统计, 径迹在簇射量能器和气体阈契仑柯夫探测器理的信息被用来完成粒子鉴别的任务. 通过优化粒子在两种探测器里的信息筛选条件, 可以得到较高的电子接收效率和π的去除能力. 因为探测器的分辨能力与粒子的动量等运动学参量有关, 所以对于不同的数据的粒子鉴别条件分别进行了优化, 并得到了对应的电子接收效率和π的去除能力.     

BESⅡ物理分析中的多变量粒子鉴别方法

以北京谱仪(BESⅡ)上常用的粒子鉴别方法(联合TOF测得的飞行时间和MDC的dE/dx)为基础,尝试利用TOF的Q值和BSC的沉积能量,发展基于多变量的粒子鉴别.将新的方法应用于p/p的鉴别,对粒子鉴别的效率、误判率和引进的系统误差进行了仔细的研究,证明均比原来有所改善     

基于共轭梯度优化算法的BP神经网络在高能粒子鉴别等领域中的应用

人工神经网络方法已被引入高能物理实验领域并被广泛地应用于夸克胶子喷注的鉴别、电子强子分辨、顶夸克和Higgs粒子的寻找等等。本文采用了一种改良的共轭梯度优化算法并应用于高能物理实验中粒子的鉴别。在该应用中,此算法既能实现每步迭代时在搜索方向上获得最优步长,又能避免目标函数陷入局部收敛点,从而使目标函数快速收敛,提高了算法的有效性。分析结果表明,我们改进后的BP算法显著地提高了粒子物理数据分析中的粒子鉴别能力。     

有倾向性重连产生的反匹配网络

在具有网络结构的系统中度关联属性对于动力学行为具有重要的影响, 所以产生适当度关联网络的方法对于大量网络系统的研究具有重要的作用. 尽管产生正匹配网络的方法已经得到很好的验证, 但是产生反匹配网络的方法还没有被系统的讨论过. 重新连接网络中的边是产生度关联网络的一个常用方法. 这里我们研究使用重连方法产生反匹配无标度网络的有效性. 我们的研究表明, 有倾向的重连可以增强网络的反匹配属性. 但是有倾向重连不能使皮尔森度相关系数下降到-1, 而是存在一个依赖于网络参数的最小值. 我们研究了网络的主要参数对于网络度相关系数的影响, 包括网络尺寸, 网络的连接密度和网络节点的度差异程度. 研究表明在网络尺寸大的情况下和节点度差异性强的情况下, 重连的效果较差. 我们研究了真实Internet网络, 发现模型产生的网络经过重连不能达到真实网络的度关联系数.     

Enhancing synchronizability by rewiring networks

<正>According to different forms of synchronized region,complex networks are divided into typeⅠ(unbounded synchronization region) and typeⅡ(bounded synchronization region) networks.This paper presents a rewiring algorithm to enhance the synchronizability of typeⅠand typeⅡnetworks.By utilizing the algorithm for an unweighted and undirected network,a better synchronizability of network with the same number of nodes and edges can be obtained. Numerical simulations on several different network models are used to support the proposed procedure.The relationship between different topological properties of the networks and the number of rewirings are shown.It finds that the final optimized network is independent of the initial network,and becomes homogeneous.In addition the optimized networks have similar structural properties in the sense of degree,and node and edge betweenness centralities.However,they do not have similar cluster coefficients for typeⅡnetworks.The research may be useful for designing more synchronizable networks and understanding the synchronization behaviour of networks.     

Transforming a complex network to an acyclic one

Acyclic networks are a class of complex networks in which links are directed and do not have closed loops. Here we present an algorithm for transforming an ordinary undirected complex network into an acyclic one. Further analysis of an acyclic network allows one to find the structural properties of the network. With our approach one can find the communities and key nodes in complex networks. Also we propose a new parameter of complex networks which can mark the most vulnerable nodes of the system. The proposed algorithm can be applied to finding communities and bottlenecks in general complex networks.     

Network Entropy Based on Topology Configuration and Its Computation to Random Networks

A definition of network entropy is presented, and as an example, the relationship between the value of network entropy of ER network model and the connect probability p as well as the total nodes N is discussed. The theoretical result and the simulation result based on the network entropy of the ER network are in agreement well with each other. The result indicated that different from the other network entropy reported before, the network entropy defined here has an obvious difference from different type of random networks or networks having different total nodes. Thus, this network entropy may portray the characters of complex networks better. It is also pointed out that, with the aid of network entropy defined, the concept of equilibrium networks and the concept of non-equilibrium networks may be introduced, and a quantitative measurement to describe the deviation to equilibrium state of a complex network is carried out.     

Weighted coupling for geographical networks: Application to reducing consensus time in sensor networks

Although many complex real-world networks are weighted, unweighted networks are used in many applications such as sensor networks. In this Letter it is shown using properly weighted networks the performance can be greatly enhanced by reducing the time necessary for the average consensus. Random geographical models are adapted as network models and a method based on mutually coupled phase oscillators is used for providing average consensus over the network. The consensus time is calculated by numerically solving the network's differential equations and monitoring the average error. The simulation results on some sample networks show that the consensus time is dramatically reduced when the proposed weights are used for the links of the underlying network.     

Assortative mixing in networks

A network is said to show assortative mixing if the nodes in the network that have many connections tend to be connected to other nodes with many connections. Here we measure mixing patterns in a variety of networks and find that social networks are mostly assortatively mixed, but that technological and biological networks tend to be disassortative. We propose a model of an assortatively mixed network, which we study both analytically and numerically. Within this model we find that networks percolate more easily if they are assortative and that they are also more robust to vertex removal.     

On the degree distribution of projected networks mapped from bipartite networks

Many real-world systems can be represented by bipartite networks. In a bipartite network, the nodes are divided into two disjoint sets, and the edges connect nodes that belong to different sets. Given a bipartite network (i.e. two-mode network) it is possible to construct two projected networks (i.e. one-mode networks) where each one is composed of only one set of nodes. While network analyses have focused on unipartite networks, considerably less attention has been paid to the analytical study of bipartite networks. Here, we analytically derive simple mathematical relationships that predict degree distributions of the projected networks by only knowing the structure of the original bipartite network. These analytical results are confirmed by computational simulations using artificial and real-world bipartite networks from a variety of biological and social systems. These findings offer in our view new insights into the structure of real-world bipartite networks.     

Network topological optimization for packet routing using multi-objective simulated annealing method

A new multi-objective simulated annealing (MOSA) algorithm is proposed for optimizing network topology. In this paper, the MOSA algorithm is used to perform two-objective simultaneous optimization. The two objectives examined in this paper are the critical packet generation rate, and average number of overall packet loads. Our results indicate that homogeneous networks can support a large critical packet generation rate under the congestion-free state, but the networks must be able to sustain relatively heavy packet load pressure if the same packet generation rate is assigned to a more heterogeneous network. At the same time, it is also found that heterogeneous networks can relieve packet load pressure, but the network is likely to become congested due to an abrupt increase of packet loads. We find that when the network size is large, lowering the average number of packet loads and raising the critical packet generation rate need not to be compromised too much. We also point out that networks can be more robust to abrupt increase of packet loads if networks are structured more homogeneously in the process of network size increment.     

Particle identification using artificial neural networks at BESⅢ

A multilayered perceptrons' neural network technique has been applied in the particle identification at BESⅢ. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples.     

面向成本-收益好的无标度耦合网络构建方法

较大平均路径长度的网络会带来较大的网络延迟, 难以支持时间敏感业务与应用. 通过增加连接可以降低源和目的节点之间的跳数, 进而降低网络平均延迟, 使得更加快速地传播信息, 但是增加连接的同时也增加了网络构建成本. 分层网络是研究网络耦合的一个有效方法, 但目前网络构建过程中将每层网络分别处理并认为每层网络之间没有强相关性. 本文提出了一种面向成本-收益的无标度网络动态构建方法. 此方法将网络分为多层, 基于连续论在高层网络中添加连接, 使得网络演化为无标度网络. 此连续过程包括节点度增加过程和局部网络半径增长两个连续过程, 在增加连接的过程中引入表征网络构建成本和收益的成本-收益指标. 模拟结果表明引入成本-收益指标的无标度耦合网络构建方法能够在合理范围内有效降低网络平均路径长度, 提升网络性能, 并且本文给出了耦合网络的动态业务性能, 通过调整高层网络避免网络拥塞.