English

Higher-order localization landscape theory of Anderson localization

Disordered Systems and Neural Networks 2024-12-31 v2 Mathematical Physics math.MP

Abstract

For a Hamiltonian H^{\hat H} containing a position-dependent (disordered) potential, we introduce a sequence of landscape functions un(r)u_n(\vec{r}) obeying H^un(r)=un1(r){\hat H} u_n(\vec{r}) = u_{n-1}(\vec{r}) with u0(r)=1u_0(\vec{r}) = 1. For nn \to \infty, 1/vn(r)=un1(r)/un(r)1/v_n(\vec{r}) = u_{n-1}(\vec{r})/u_{n}(\vec{r}) converges to the lowest eigenenergy E1E_1 of H^{\hat H} whereas u(r)u_{\infty}(\vec{r}) yields the corresponding wave function ψ1(r)\psi_1(\vec{r}). For large but finite nn, vn(r)v_n(\vec{r}) can be approximated by a piecewise constant function vn(r)vn(m)v_n(\vec{r}) \simeq v_n^{(m)} for rΩm\vec{r} \in \Omega_m and yields progressively improving estimations of eigenenergies Em=1/vn(m)E_m = 1/v_n^{(m)} of locally fundamental eigenstates ψm(r)un(r)\psi_m(\vec{r}) \propto u_{n}(\vec{r}) in spatial domains Ωm\Omega_m. These general results are illustrated by a number of examples in one dimension: box potential, sequence of randomly placed infinite potential barriers, smooth and spatially uncorrelated random potentials, quasiperiodic potential, as well as for the uncorrelated random potential in two dimensions.

Keywords

Cite

@article{arxiv.2408.05078,
  title  = {Higher-order localization landscape theory of Anderson localization},
  author = {Sergey E. Skipetrov},
  journal= {arXiv preprint arXiv:2408.05078},
  year   = {2024}
}

Comments

10 pages, 5 figures