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Arezoo Afshar

Publications and source records attributed to Arezoo Afshar.

2 recordsLinked to original sources

Magnetometry with Broadband Microwave Fields in Nitrogen-Vacancy Centers in Diamond

Nitrogen-vacancy (NV) centers in diamond are optically addressable and versatile light-matter interfaces with practical application in magnetic field sensing, offering the ability to operate at room temperature and reach sensitivities below pT/$\sqrt{\mathrm{Hz}}$. We propose an approach to simultaneously probe all of the magnetically sensitive states using a broadband microwave field and demonstrate that it can be used to measure the external DC magnetic field strength with sensitivities on the order of 10~pT/$\sqrt{\mathrm{Hz}}$. We develop tools for analyzing the temporal signatures in the transmitted broadband microwaves to estimate the magnetic field, comparing maximum likelihood estimation based on minimizing the Kullback-Leibler divergence to various neural network models, and both methods independently reach practical sensitivities. These results are achieved without optimizing parameters such as the bandwidth, and shape of the probing microwave field such that further improvements in sensitivity can be envisioned. Our results motivate novel implementations of NV-based magnetic sensors with the potential for vectorial magnetic field detection at 1-10 kHz update rates and improved sensitivities without requiring a bias magnetic field.

cond-mat.mes-hall

Vector Magnetometry with Broadband Microwave Fields in Nitrogen-Vacancy Centers in Diamond

We present a novel method for full vector magnetometry using nitrogen-vacancy (NV) centers. In contrast to conventional optically detected magnetic resonance techniques, our method employs two distinct broadband microwave pulses and measures them after transmission through the NV sensor medium, thus capturing the line splitting of the ground state triplet due to the Zeeman effect. Two orthogonally polarized microwave pulses allow resolving all magnetic field components independently by reading out differently oriented NV centers. Simulated data is analyzed using deep neural networks, whose efficacy we expect to translate very well to experiments. Our method yields sensitivities between $5~\mathrm{pT}/\sqrt{\mathrm{Hz}}$ and $100~\mathrm{pT}/\sqrt{\mathrm{Hz}}$ across different magnetic field vector components, while achieving approximately $\mathrm{nT}$ accuracy at a signal-to-noise (SNR) ratio of $70~\mathrm{dB}$. By being capable of accurately measuring magnetic fields down to $25~\mathrm{μT}$, the need for a bias field beyond Earth's magnetic field is eliminated.

quant-ph