空间相关分支过程的渐近性和临界性

Ilie Grigorescu, Min Kang
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引用次数: 1

摘要

摘要研究了一个非保守半群(St)t≥0,该半群是由在Rd域中运动的粒子的分支过程确定的。当一个粒子在边界处被杀死时,在该区域的随机点上产生了平均数为K¯的新一代粒子。在分支之间,粒子由具有狄利克雷边界条件的扩散过程驱动。根据K¯−1的符号,我们区分了超/次临界状态,并确定了粒子总数n(t) ~ exp (α∗t)的确切指数率,其中α∗显式依赖于K¯。证明了Yaglom极限St/n(t)→ν,其中拟平稳分布ν由Dirichlet核在点α∗处的解决定。主要应用于粒子系统,其中半群的总质量归一化给出了贝克-斯奈彭分支扩散(BSBD)的流体动力学极限。由于ν是平衡状态下的渐近剖面,而拟平稳分布族ν由K¯索引,因此该模型提供了一个明确的自组织临界性的例子。关键词:SemigroupYaglom限制分支过程supercriticalqsdbak - sneppenflefleming - viotdirichlet核关键词:一级:60j3560j80二级:47D0760K35披露声明作者未报告潜在利益冲突。
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Asymptotics and criticality for a space-dependent branching process
AbstractWe investigate a non-conservative semigroup (St)t≥0 determined by a branching process tracing the evolution of particles moving in a domain in Rd. When a particle is killed at the boundary, a new generation of particles with mean number K¯ is born at a random point in the domain. Between branching, the particles are driven by a diffusion process with Dirichlet boundary conditions. According to the sign of K¯−1, we distinguish super/sub-critical regimes and determine the exact exponential rate for the total number of particles n(t)∼exp⁡(α∗t), with α∗ depending explicitly on K¯. We prove the Yaglom limit St/n(t)→ν, where the quasi-stationary distribution ν is determined by the resolvent of the Dirichlet kernel at the point α∗. The main application is in particle systems, where the normalization of the semigroup by its total mass gives the hydrodynamic limit of the Bak-Sneppen branching diffusions (BSBD). Since ν is the asymptotic profile under equilibrium, and the family of quasi-stationary distributions ν is indexed by K¯, the model provides an explicit example of self-organized criticality.Keywords: SemigroupYaglom limitbranching processessupercriticalqsdBak-SneppenFleming-ViotDirichlet kernelKey Words and Phrases: Primary: 60J3560J80Secondary: 47D0760K35 Disclosure statementNo potential conflict of interest was reported by the author(s).
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来源期刊
CiteScore
1.90
自引率
0.00%
发文量
42
审稿时长
>12 weeks
期刊介绍: Stochastics: An International Journal of Probability and Stochastic Processes is a world-leading journal publishing research concerned with stochastic processes and their applications in the modelling, analysis and optimization of stochastic systems, i.e. processes characterized both by temporal or spatial evolution and by the presence of random effects. Articles are published dealing with all aspects of stochastic systems analysis, characterization problems, stochastic modelling and identification, optimization, filtering and control and with related questions in the theory of stochastic processes. The journal also solicits papers dealing with significant applications of stochastic process theory to problems in engineering systems, the physical and life sciences, economics and other areas. Proposals for special issues in cutting-edge areas are welcome and should be directed to the Editor-in-Chief who will review accordingly. In recent years there has been a growing interaction between current research in probability theory and problems in stochastic systems. The objective of Stochastics is to encourage this trend, promoting an awareness of the latest theoretical developments on the one hand and of mathematical problems arising in applications on the other.
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