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On Mesoscopic Equilibrium for Linear Statistics in Dyson’s Brownian Motion


About this Title

Maurice Duits and Kurt Johansson

Publication: Memoirs of the American Mathematical Society
Publication Year: 2018; Volume 255, Number 1222
ISBNs: 978-1-4704-2964-5 (print); 978-1-4704-4821-9 (online)
DOI: https://doi.org/10.1090/memo/1222
Published electronically: August 20, 2018
Keywords:Dyson’s Brownian motion, Linear Statistics, Central Limit Theorems, Mesoscopic scale, Random Matrices, Poisson initial conditions

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Table of Contents


Chapters

  • Chapter 1. Introduction
  • Chapter 2. Statement of results
  • Chapter 3. Proof of Theorem 2.1
  • Chapter 4. Proof of Theorem 2.3
  • Chapter 5. Asymptotic analysis of $K_n$ and $R_n$
  • Chapter 6. Proof of Proposition 2.4
  • Chapter 7. Proof of Lemma 4.3
  • Chapter 8. Random initial points
  • Chapter 9. Proof of Theorem 2.6: the general case
  • Appendix

Abstract


In this paper we study mesoscopic fluctuations for Dyson's Brownian motion with . Dyson showed that the Gaussian Unitary Ensemble (GUE) is the invariant measure for this stochastic evolution and conjectured that, when starting from a generic configuration of initial points, the time that is needed for the GUE statistics to become dominant depends on the scale we look at: The microscopic correlations arrive at the equilibrium regime sooner than the macrosopic correlations. In this paper we investigate the transition on the intermediate, i.e. mesoscopic, scales. The time scales that we consider are such that the system is already in microscopic equilibrium (sine-universality for the local correlations), but we have not yet reached equilibrium at the macrosopic scale. We describe the transition to equilibrium on all mesoscopic scales by means of Central Limit Theorems for linear statistics with sufficiently smooth test functions. We consider two situations: deterministic initial points and randomly chosen initial points. In the random situation, we obtain a transition from the classical Central Limit Theorem for independent random variables to the one for the GUE.

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