REVIEW 1 cited by
Localization of light in three dimensions: a mobility edge in the imaginary axis in non-Hermitian Hamiltonians
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Searching for Anderson localization of light in three dimensions has challenged experimental and theoretical research for the last decades. Here the problem is analyzed through large scale numerical simulations, using a radiative Hamiltonian i.e. a non-Hermitian long-range hopping Hamiltonian, well suited to model light-matter interaction in cold atomic clouds. Light interaction in atomic clouds is considered in presence of positional and diagonal disorder. Due to the interplay of disorder and cooperative effects (sub- and super-radiance) a novel type of localization transition is shown to emerge, differing in several aspects from standard localization transitions which occur along the real energy axis. The localization transition discussed here is characterized by a mobility edge along the imaginary energy axis of the eigenvalues which is mostly independent from the real energy value of the eigenmodes. Differently from usual mobility edges it separates extended states from hybrid localized states and it manifest itself in the large moments of the participation ratio of the eigenstates. Our prediction of a mobility edge in the imaginary axis, i.e. depending on the eigenmode lifetime, paves the way to achieve control both in the time and space domain of open quantum systems.
Forward citations
Cited by 1 Pith paper
-
Spectral distribution of sparse Gaussian Ensembles of Real Asymmetric Matrices
A complexity-parameter evolution equation is used to derive spectral densities for sparse real-asymmetric Gaussian ensembles, with numerical tests that fit several constants to the data.
Discussion (0). Continue with ORCID to comment.