Localization tensor revisited: geometric-probabilistic foundations and a structure factor criterion under periodic boundaries

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Authors: Zhe-Hao Zhang, Xiaoming Cai, Yi-Cong Yu
Cited by: 0
References: 57

Abstract

Abstract We revisit the localization tensor (LT) from geometric and probabilistic perspectives and construct extensions that are naturally compatible with periodic boundary conditions (PBC), without redefining the position operator. In open boundary conditions, we show that the LT can be written exactly as the covariance of a bivariate probability distribution built from density–density correlations. This leads to two conceptually distinct extensions to PBC: (i) a geometric one based on the Riemannian center (Frëchet mean) on the circle, and (ii) a metric-free one based on the mutual information I , which treats the configuration space purely as a probability space. We then relate the LT to the static structure factor by identifying the diagonal part, C pp , as a ‘localization function’ C ( p ), whose small-momentum behavior determines the LT in the thermodynamic limit. This clarifies why the LT is sensitive to transitions out of the extended phase but by itself cannot distinguish Anderson-type localization from dimerization: both share the same low-momentum asymptotics. We show that the finite-momentum behavior of C ( p ), together with an inverse participation ratio (IPR) based upper bound valid in localized phases, provides a sharp criterion that discriminates localization from dimerization. These results are illustrated on the Su–Schrieffer–Heeger and Aubry–Andrë models, with and without interactions, and suggest that structure factor based probes offer robust and experimentally accessible diagnostics of localized and dimerized phases under PBC.

Fields of Study & Subjects

Quantum many-body systems Spectral Theory in Mathematical Physics Quantum and electron transport phenomena

Publication details

Journal / source
Physica Scripta
Volume
101
Issue
8
Pages
085910–085910
Publication date
16 February 2026
Language
EN