Asymptotics of the determinant of the modified Bessel functions and the second Painlevé equation

IF 0.9 4区 数学 Q4 PHYSICS, MATHEMATICAL
Yu Chen, Shuai-Xia Xu, Yu-Qiu Zhao
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引用次数: 0

Abstract

In the paper, we consider the extended Gross–Witten–Wadia unitary matrix model by introducing a logarithmic term in the potential. The partition function of the model can be expressed equivalently in terms of the Toeplitz determinant with the (i,j)-entry being the modified Bessel functions of order ijν, ν. When the degree n is finite, we show that the Toeplitz determinant is described by the isomonodromy τ-function of the Painlevé III equation. As a double scaling limit, we establish an asymptotic approximation of the logarithmic derivative of the Toeplitz determinant, expressed in terms of the Hastings–McLeod solution of the inhomogeneous Painlevé II equation with parameter ν+12. The asymptotics of the leading coefficient and recurrence coefficient of the associated orthogonal polynomials are also derived. We obtain the results by applying the Deift–Zhou nonlinear steepest descent method to the Riemann–Hilbert problem for orthogonal polynomials on the Hankel loop. The main concern here is the construction of a local parametrix at the critical point z=1, where the ψ-function of the Jimbo–Miwa Lax pair for the inhomogeneous Painlevé II equation is involved.

修正贝塞尔函数行列式的渐近性和第二个潘列夫方程
在本文中,我们通过在势中引入对数项来考虑扩展的格罗斯-威滕-瓦迪亚单元矩阵模型。该模型的分区函数可以等价地用托普利兹行列式来表示,其中 (i,j) 项是阶数为 i-j-ν, ν∈ℂ 的修正贝塞尔函数。当度 n 有限时,我们证明托普利兹行列式是由潘列韦三世方程的等单调性 τ 函数描述的。作为双重缩放极限,我们建立了托普利兹行列式对数导数的渐近近似,用参数为 ν+12 的非均质佩恩列韦 II 方程的黑斯廷斯-麦克里奥德解来表示。我们还推导出了相关正交多项式的领先系数和递推系数的渐近线。我们将 Deift-Zhou 非线性最陡下降法应用于汉克尔环上正交多项式的黎曼-希尔伯特问题,从而得到了这些结果。这里主要关注的是临界点 z=-1 的局部参数矩阵的构建,其中涉及不均匀 Painlevé II 方程的 Jimbo-Miwa Lax 对的ψ函数。
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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.
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