{ "id": "1412.7108", "version": "v1", "published": "2014-12-22T19:33:48.000Z", "updated": "2014-12-22T19:33:48.000Z", "title": "The eigenvectors of Gaussian matrices with an external source", "authors": [ "Romain Allez", "Joël Bun", "Jean-Philippe Bouchaud" ], "comment": "27 pages, 4 figures", "categories": [ "math.PR", "cond-mat.stat-mech" ], "abstract": "We consider a diffusive matrix process $(X_t)_{t\\ge 0}$ defined as $X_t:=A+H_t$ where $A$ is a given deterministic Hermitian matrix and $(H_t)_{t\\ge 0}$ is a Hermitian Brownian motion. The matrix $A$ is the \"external source\" that one would like to estimate from the noisy observation $X_t$ at some time $t>0$. We investigate the relationship between the non-perturbed eigenvectors of the matrix $A$ and the perturbed eigenstates at some time $t$ for the three relevant scaling relations between the time $t$ and the dimension $N$ of the matrix $X_t$. We determine the asymptotic (mean-squared) projections of any given non-perturbed eigenvector $|\\psi_j^0>$, associated to an eigenvalue $a_j$ of $A$ which may lie inside the bulk of the spectrum or be isolated (spike) from the other eigenvalues, on the orthonormal basis of the perturbed eigenvectors $|\\psi_i^t>,i\\neq j$. In the case of one isolated eigenvector $|\\psi_j^0>$, we prove a central limit Theorem for the overlap $< \\psi_j^0|\\psi_j^t>$. When properly centered and rescaled by a factor $\\sqrt{N}$, this overlap converges in law towards a centered Gaussian distribution with an explicit variance depending on $t$. Our method is based on analyzing the eigenvector flow under the Dyson Brownian motion.", "revisions": [ { "version": "v1", "updated": "2014-12-22T19:33:48.000Z" } ], "analyses": { "keywords": [ "external source", "gaussian matrices", "dyson brownian motion", "deterministic hermitian matrix", "hermitian brownian motion" ], "note": { "typesetting": "TeX", "pages": 27, "language": "en", "license": "arXiv", "status": "editable", "adsabs": "2014arXiv1412.7108A" } } }