Sharp asymptotics for Toeplitz determinants, fluctuations and the gaussian free field on a Riemann surface
Robert J. Berman

TL;DR
This paper establishes sharp asymptotics and a CLT for determinantal point processes on Riemann surfaces, linking fluctuations to the Gaussian free field and deriving a new Szeg"o theorem with applications to Toeplitz determinants.
Contribution
It introduces a new sharp strong Szeg"o type theorem for Toeplitz determinants on Riemann surfaces with constant curvature, and proves a CLT for fluctuations of point processes in this setting.
Findings
Fluctuations converge to the Laplacian of the Gaussian free field.
Exponential concentration of measure properties are established.
New Bergman kernel asymptotics with exponentially small errors.
Abstract
We consider canonical determinantal random point processes with N particles on a compact Riemann surface X defined with respect to the constant curvature metric. In the higher genus (hyperbolic) cases these point processes may be defined in terms of automorphic forms. We establish strong exponential concentration of measure type properties involving Dirichlet norms of linear statistics. This gives an optimal Central Limit Theorem (CLT), saying that the fluctuations of the corresponding empirical measures converge, in the large N limt, towards the Laplacian of the Gaussian free field on X in the strongest possible sense. The CLT is also shown to be equivalent to a new sharp strong Szeg\"o type theorem for Toeplitz determinants in this context. One of the ingredients in the proofs are new Bergman kernel asymptotics providing exponentially small error terms in a constant curvature setting.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGeometry and complex manifolds · Random Matrices and Applications · Stochastic processes and statistical mechanics
