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arXiv 2201.05377math.PR
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幂律更新障碍物下一维简单随机游走的局域化

Localization of a one-dimensional simple random walk among power-law renewal obstacles

Julien Poisat, Francois Simenhaus

AI总结:

该研究针对幂律更新障碍物下的一维淬灭软障碍随机游走,证明存活游走会局域于最优间隙,建立了更新尾指数不同取值下的行为二分性,拓展了随机游走与有向聚合物局域化研究。

摘要:

我们研究被淬灭软障碍物杀死的一维简单随机游走。障碍物的位置由增量服从幂律分布的更新过程生成。在之前的工作中,我们计算了淬灭存活概率的大时间渐近行为。在本工作中,我们通过描述存活条件下随机游走的行为继续展开研究。我们证明,在大概率下,游走会快速到达一个唯一的、随时间变化的无障碍物最优间隙,并在该处局域化。我们实际上建立了一种二分性:若更新尾指数小于1,则游走到达最优间隙后,剩余时间几乎都在间隙内度过,仅会有限次访问间隙底部;若更新尾指数大于1,则随机游走大部分时间处于最优间隙内,但也会进行短程向外偏移,我们给出了这些偏移的长度和数量的匹配上下界。我们的核心工具包括存活概率的马尔可夫更新解释,以及障碍物环境的各类比较论证。我们的结果也可以用多排斥界面下有向聚合物的局域化性质来重新表述。

英文摘要:

We consider a one-dimensional simple random walk killed by quenched soft obstacles. The position of the obstacles is drawn according to a renewal process with a power-law increment distribution. In a previous work, we computed the large-time asymptotics of the quenched survival probability. In the present work we continue our study by describing the behaviour of the random walk conditioned to survive. We prove that with large probability, the walk quickly reaches a unique time-dependent optimal gap that is free from obstacle and gets localized there. We actually establish a dichotomy. If the renewal tail exponent is smaller than one then the walk hits the optimal gap and spends all of its remaining time inside, up to finitely many visits to the bottom of the gap. If the renewal tail exponent is larger than one then the random walk spends most of its time inside of the optimal gap but also performs short outward excursions, for which we provide matching upper and lower bounds on their length and cardinality. Our key tools include a Markov renewal interpretation of the survival probability as well as various comparison arguments for obstacle environments. Our results may also be rephrased in terms of localization properties for a directed polymer among multiple repulsive interfaces.

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