Level-spacing distributions of the Gaussian unitary random matrix ensemble
arXiv:cond-mat/0211279 · doi:10.1002/pssb.200404784
Abstract
Level-spacing distributions of the Gaussian Unitary Ensemble (GUE) of random matrix theory are expressed in terms of solutions of coupled differential equations. Series solutions up to order 50 in the level spacing are obtained, thus providing a very good description of the small-spacing part of the level-spacing distribution, which can be used to make comparisons with experimental or numerical data. The level-spacing distributions can be obtained by solving the system of differential equations numerically.
arXiv commentThe series expansions discussed in this paper are available as Mathematica code at http://mcs.open.ac.uk/ugg2/RMT/LevelSpacingGUE.m (16kB)
References in corpus (10)
- Random Matrix Theories in Quantum Physics: Common Concepts
- Application of the -Function Theory of Painlevé Equations to Random Matrices: PIV, PII and the GUE
- Level-Spacing Distributions of Planar Quasiperiodic Tight-Binding Models
- Discrete Painlevé equations and random matrix averages
- Growth models, random matrices and Painleve transcendents
- Universal level-spacing statistics in quasiperiodic tight-binding models
- Application of random matrix theory to quasiperiodic systems
- Energy spectra and eigenstates of quasiperiodic tight-binding Hamiltonians
- Energy Levels of Quasiperiodic Hamiltonians, Spectral Unfolding, and Random Matrix Theory
- Exact Wigner surmise type evaluation of the spacing distribution in the bulk of the scaled random matrix ensembles
Cited by in corpus (6)
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- Discrete Painlevé equations and random matrix averages
- Nearest-neigbor spacing distributions of the beta-Hermite ensemble of random matrices
- Statistics of fermions in a -dimensional box near a hard wall
- Hole probability for noninteracting fermions in a -dimensional trap
- Surmise for random matrices' level spacing distributions beyond nearest-neighbors