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arXiv 2007.03594math.PRmath-phmath.MP
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通过自助法与测地线几何研究一般最后通过渗流中的最优尾指数

Optimal tail exponents in general last passage percolation via bootstrapping & geodesic geometry

Shirshendu Ganguly, Milind Hegde

AI总结:

本文针对一般权重分布的二维最后通过渗流,利用超可加性、测度集中及测地线西瓜几何,通过迭代自助法证明测地线权重具有 $3/2$ 与 $3$ 的最优上下尾指数。

摘要:

我们考虑在 $\mathbb Z^2$ 上具有一般权重分布的最后通过渗流,该模型预期属于 Kardar-Parisi-Zhang(KPZ)普适类。在此模型中,给定端点之间使与其顶点相关的独立同分布权重变量之和最大化的有向路径称为测地线。在极限测地线权重剖面具有曲率、且点对点权重的两个尾部均具有拉伸指数衰减的自然条件下,我们利用几何论证强化这些假设,证明了对所有充分大的有限 $r$,从 $(1,1)$ 到 $(r,r)$ 的测地线权重具有指数为 $3/2$ 和 $3$ 的最优上尾与下尾行为。证明融合了若干思想,包括众所周知的最后通过值的超可加性、拉伸指数随机变量之和的测度集中行为,以及来自测地线和更一般对象(称为测地线西瓜)研究的几何洞见。此前,这种最优行为仅在精确可解模型中得到,其证明依赖于对可积概率公式的艰深分析,而这些公式在一般情形下并不可用。我们的结果阐明了一类 KPZ 随机增长模型中普适性的一个方面,并为 GUE Tracy-Widom 分布的上尾和下尾指数提供了几何解释;该分布被猜想为此类模型的一点尺度极限。关键论证基于一个具有普遍意义的观察:超可加性允许一种自然的迭代自助法程序来获得改进的尾部估计。

英文摘要:

We consider last passage percolation on $\mathbb Z^2$ with general weight distributions, which is expected to be a member of the Kardar-Parisi-Zhang (KPZ) universality class. In this model, an oriented path between given endpoints which maximizes the sum of the i.i.d. weight variables associated to its vertices is called a geodesic. Under natural conditions of curvature of the limiting geodesic weight profile and stretched exponential decay of both tails of the point-to-point weight, we use geometric arguments to upgrade the assumptions to prove optimal upper and lower tail behavior with the exponents of $3/2$ and $3$ for the weight of the geodesic from $(1,1)$ to $(r,r)$ for all large finite $r$. The proofs merge several ideas, including the well known super-additivity property of last passage values, concentration of measure behavior for sums of stretched exponential random variables, and geometric insights coming from the study of geodesics and more general objects called geodesic watermelons. Previously such optimal behavior was only known for exactly solvable models, with proofs relying on hard analysis of formulas from integrable probability, which are unavailable in the general setting. Our results illustrate a facet of universality in a class of KPZ stochastic growth models and provide a geometric explanation of the upper and lower tail exponents of the GUE Tracy-Widom distribution, the conjectured one point scaling limit of such models. The key arguments are based on an observation of general interest that super-additivity allows a natural iterative bootstrapping procedure to obtain improved tail estimates.

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