经典β系综的高低温二象性

IF 0.9 4区 数学 Q4 PHYSICS, MATHEMATICAL Random Matrices-Theory and Applications Pub Date : 2022-04-05 DOI:10.1142/s2010326322500356
P. Forrester
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引用次数: 4

摘要

[公式:见文本]-集成的循环方程通常是根据[公式:见文本]展开来求解的。我们观察到,也可以固定[公式:见文]并展开[公式:见文]的反幂。在主导阶上,对于与线性统计的平均值相对应的一点函数[公式:见文],在专门研究经典权重之后,这恢复了Stieltjes关于经典多项式的零点与某些对数气体势能的最小能量配置的众所周知的结果。此外,在经典权值的情况下,由[公式:见文]所满足的微分方程(即特定的里卡蒂方程)与高温尺度极限下由[公式:见文]所满足的微分方程([公式:见文]固定,[公式:见文])之间存在着简单的关系,暗示着一定的高低温对偶性。这种对偶性的推广,在没有任何限制过程的情况下是有效的,它适用于[公式:见文]和它在经典[公式:见文]中所有更高点的类似物——系综。
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High–low temperature dualities for the classical β-ensembles
The loop equations for the [Formula: see text]-ensembles are conventionally solved in terms of a [Formula: see text] expansion. We observe that it is also possible to fix [Formula: see text] and expand in inverse powers of [Formula: see text]. At leading order, for the one-point function [Formula: see text] corresponding to the average of the linear statistic [Formula: see text] and after specialising to the classical weights, this reclaims well known results of Stieltjes relating the zeros of the classical polynomials to the minimum energy configuration of certain log–gas potential energies. Moreover, it is observed that the differential equations satisfied by [Formula: see text] in the case of classical weights — which are particular Riccati equations — are simply related to the differential equations satisfied by [Formula: see text] in the high temperature scaled limit [Formula: see text] ([Formula: see text] fixed, [Formula: see text]), implying a certain high–low temperature duality. A generalisation of this duality, valid without any limiting procedure, is shown to hold for [Formula: see text] and all its higher point analogues in the classical [Formula: see text]-ensembles.
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来源期刊
Random Matrices-Theory and Applications
Random Matrices-Theory and Applications Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.90
自引率
11.10%
发文量
29
期刊介绍: Random Matrix Theory (RMT) has a long and rich history and has, especially in recent years, shown to have important applications in many diverse areas of mathematics, science, and engineering. The scope of RMT and its applications include the areas of classical analysis, probability theory, statistical analysis of big data, as well as connections to graph theory, number theory, representation theory, and many areas of mathematical physics. Applications of Random Matrix Theory continue to present themselves and new applications are welcome in this journal. Some examples are orthogonal polynomial theory, free probability, integrable systems, growth models, wireless communications, signal processing, numerical computing, complex networks, economics, statistical mechanics, and quantum theory. Special issues devoted to single topic of current interest will also be considered and published in this journal.
期刊最新文献
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