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Latent symmetry in a minimal non-Hermitian trimer

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abstract

We study a minimal non-Hermitian trimer with latent symmetry formed by a cospectral pair of sites embedded in a three-site network with nonreciprocal couplings. We show that cospectrality provides a structural latent-symmetry constraint, whereas exact dark-state decoupling requires an additional algebraic matching condition among the couplings. For a dark-state-compatible representative of this cospectral class, the model admits an exact decomposition into dark and bright sectors: the dark mode is spectrally isolated and retains a complex eigenvalue, while the bright sector reduces to an effective non-Hermitian dimer. For a suitable choice of parameters, this reduced subsystem becomes $\mathcal{PT}$-symmetric and exhibits partial spectral reality, with two real eigenvalues coexisting with the complex dark eigenvalue. At the critical point, the bright sector hosts an embedded second-order exceptional point, which renders the full trimer defective and gives rise to the characteristic Jordan-block dynamics. These results establish the non-Hermitian trimer as a minimal analytically solvable setting in which latent symmetry, sector-resolved $\mathcal{PT}$ symmetry, and exceptional-point physics naturally coexist.

fields

cs.LG 1

years

2026 1

verdicts

UNVERDICTED 1

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Target localization, identification and sensing using latent symmetries

cs.LG · 2026-05-31 · unverdicted · novelty 8.0

Scatterer arrays with latent symmetries enable intruder localization and identification by quantifying symmetry breaking in a capacitance matrix model, with Bayesian inference and neural networks outperforming dictionary methods under noise.

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  • Target localization, identification and sensing using latent symmetries cs.LG · 2026-05-31 · unverdicted · none · ref 5 · internal anchor

    Scatterer arrays with latent symmetries enable intruder localization and identification by quantifying symmetry breaking in a capacitance matrix model, with Bayesian inference and neural networks outperforming dictionary methods under noise.