Noise Constraints for Nonlinear Exceptional Point Sensing

TL;DR

This paper investigates how noise affects nonlinear exceptional point sensors, revealing that noise can displace and diminish the EP's benefits, challenging previous assumptions about their enhanced sensing capabilities.

Contribution

The study uncovers how noise interacts with nonlinearity in EP sensors, showing it can reduce the EP's order and negate divergence benefits, which was previously unrecognized.

Findings

Noise displaces the EP in parameter space.

Noise reduces the EP's order, eliminating divergence.

Noise divergence near nonlinear EP exceeds standard predictions.

Abstract

Exceptional points (EPs) are singularities in the parameter space of a non-Hermitian system where eigenenergies and eigenstates coincide. They hold promise for enhancing sensing applications, but this is limited by the divergence of shot noise near EPs. According to recent studies, EP sensors operating in the nonlinear regime may avoid these limitations. By analyzing an exemplary nonlinear system, we show that the interplay of noise and nonlinearity introduces previously-unidentified obstacles to enhanced sensing. The noise effectively displaces the EP in parameter space and reduces its order, thereby eliminating the sought-for divergence in the signal-to-noise ratio. Moreover, the noise near the nonlinear EP experiences a stronger divergence than predicted by standard calculations of the Petermann noise factor, due to the properties of the Bogoliubov-de Gennes Hamiltonian governing the fluctuations. Our semi-analytical estimates for the noise level agree quantitatively with the results of stochastic numerical simulations.