Real-Time Magnetometry Using Dark States of a Nitrogen Vacancy Center

TL;DR

This paper demonstrates a real-time magnetometry technique using nitrogen vacancy centers by applying Bayesian inference to photon count data, enabling dynamic magnetic field estimation with minimal photon detection.

Contribution

It introduces a novel real-time magnetometry method leveraging dark states of NV centers and Bayesian inference, improving dynamic field estimation accuracy.

Findings

Effective real-time magnetic field estimation from photon counts.

Bayesian estimator adapts to time-varying fields modeled as Ornstein-Uhlenbeck processes.

Single-photon detection suffices for accurate field updates.

Abstract

We demonstrate real-time magnetometry by detecting fluorescence from a nitrogen vacancy center in the setting of coherent population trapping and by estimating magnetic field from the time series of the observed photon counts, which are correlated with the underlying field. The proof-of-principle experiment uses an external time-varying magnetic field that follows an Ornstein-Uhlenbeck (OU) process. By taking into consideration the statistical properties of the OU process, a Bayesian inference-based estimator can effectively update dynamical information of the field in real time with the detection of just a single photon.