Local resetting in non-conserving zero-range processes with extensive rates

IF 1.1 Q3 PHYSICS, MULTIDISCIPLINARY
Pascal Grange
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Abstract

A non-conserving zero-range process with extensive creation, annihilation and hopping rates is subjected to local resetting. The model is formulated on a large, fully-connected network of states. The states are equipped with a (bounded) fitness level: particles are added to each state at a rate proportional to the fitness level of the state. Moreover, particles are annihilated at a constant rate, and hop at a fixed rate to a uniformly-drawn state in the network. This model has been interpreted in terms of population dynamics: the fitness is the reproductive fitness in a haploid population, and the hopping process models mutation. It has also been interpreted as a model of network growth with a fixed set of nodes (in which particles occupying a state are interpreted as links pointing to this state). In the absence of resetting, the model is known to reach a steady state, which in a certain limit may exhibit a condensate at maximum fitness. If the model is subjected to global resetting by annihilating all particles at Poisson-distributed times, there is no condensation in the steady state. If the system is subjected to local resetting, the occupation numbers of each state are reset to zero at independent random times. These times are distributed according to a Poisson process whose rate (the resetting rate) depends on the fitness. We derive the evolution equation satisfied by the probability law of the occupation numbers. We calculate the average occupation numbers in the steady state. The existence of a condensate is found to depend on the local behavior of the resetting rate at maximum fitness: if the resetting rate vanishes at least linearly at high fitness, a condensate appears at maximum fitness in the limit where the sum of the annihilation and hopping rates is equal to the maximum fitness.
具有广泛速率的非守恒零程过程中的局部重置
非守恒零程过程具有广泛的创造、湮灭和跳跃率,并受到局部重置的影响。该模型是在一个大型、完全连接的状态网络上建立的。这些状态都有一个(有约束的)适应度水平:粒子以与状态适应度水平成正比的速率加入每个状态。此外,粒子以恒定的速率湮灭,并以固定的速率跳转到网络中统一绘制的状态。这个模型被解释为种群动力学模型:适合度是单倍体种群的繁殖适合度,跳跃过程是突变的模型。它也被解释为一个具有固定节点集的网络增长模型(其中占据某一状态的粒子被解释为指向该状态的链接)。众所周知,在没有重置的情况下,该模型会达到一个稳定状态,并在一定限度内表现出最大适合度的凝聚态。如果模型在泊松分布的时间内湮灭所有粒子,从而进行全局重置,稳态中就不会出现凝集。如果系统进行局部重置,则每个状态的占据数都会在独立的随机时间重置为零。这些时间按照泊松过程分布,泊松过程的速率(重置速率)取决于适应度。我们推导出占领数概率规律所满足的演化方程。我们计算了稳定状态下的平均占据数。我们发现凝聚态的存在取决于重置率在最大适合度时的局部行为:如果重置率在高适合度时至少线性消失,那么在湮灭率和跳跃率之和等于最大适合度的极限时,凝聚态就会出现在最大适合度处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Physics Communications
Journal of Physics Communications PHYSICS, MULTIDISCIPLINARY-
CiteScore
2.60
自引率
0.00%
发文量
114
审稿时长
10 weeks
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