Critical exponents of correlated percolation of sites not visited by a random walk

IF 2.4 3区 物理与天体物理 Q1 Mathematics
Raz Halifa Levi, Yacov Kantor
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引用次数: 0

Abstract

We consider a d-dimensional correlated percolation problem of sites not visited by a random walk on a hypercubic lattice Ld for d=3, 4, and 5. The length of the random walk is N=uLd. Close to the critical value u=uc, many geometrical properties of the problem can be described as powers (critical exponents) of ucu, such as β, which controls the strength of the spanning cluster, and γ, which characterizes the behavior of the mean finite cluster size S. We show that at uc the ratio between the mean mass of the largest cluster M1 and the mass of the second largest cluster M2 is independent of L and can be used to find uc. We calculate β from the L dependence of M1 and M2, and γ from the finite size scaling of S. The resulting exponent β remains close to 1 in all dimensions. The exponent γ decreases from 3.9 in d=3 to 1.9 in d=4 and 1.3 in d=5 towards γ=1 expected in d=6, which is close to γ=4/(d2).

Abstract Image

随机漫步未访问地点的相关渗流临界指数
我们考虑一个 d 维的相关渗滤问题,即在 d=3、4 和 5 的超立方晶格 Ld 上,随机行走未访问的点的渗滤问题。随机行走的长度为 N=uLd。在临界值 u=uc 附近,问题的许多几何特性都可以用 uc-u 的幂次(临界指数)来描述,如控制跨簇强度的 β 和描述平均有限簇大小 S 行为的 γ。我们证明,在 uc 时,最大簇 M1 的平均质量与第二大簇 M2 的质量之比与 L 无关,可以用来求出 uc。我们根据 M1 和 M2 与 L 的关系计算出 β,并根据 S 的有限大小缩放计算出 γ。指数γ从 d=3 时的≈3.9 下降到 d=4 时的≈1.9 和 d=5 时的≈1.3,最终在 d=6 时达到预期的 γ=1,接近 γ=4/(d-2)。
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来源期刊
Physical review. E
Physical review. E 物理-物理:流体与等离子体
CiteScore
4.60
自引率
16.70%
发文量
0
审稿时长
3.3 months
期刊介绍: Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.
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