Toward deep-learning-assisted spectrally-resolved imaging of magnetic noise

TL;DR

This paper demonstrates how deep neural networks can be used to efficiently and accurately reconstruct spectral densities of magnetic noise from color-center-based measurements, enabling automated nanoscale imaging.

Contribution

It introduces a machine learning approach to extract spectral densities from noisy measurements, reducing the need for experimenter input and enabling automated imaging protocols.

Findings

Neural network accurately reconstructs spectral densities

Method works with minimal data and high noise levels

Applicable to various types of magnetic stimuli

Abstract

Recent progress in the application of color centers to nanoscale spin sensing makes the combined use of noise spectroscopy and scanning probe imaging an attractive route for the characterization of arbitrary material systems. Unfortunately, the traditional approach to characterizing the environmental magnetic field fluctuations from the measured probe signal typically requires the experimenter's input, thus complicating the implementation of automated imaging protocols based on spectrally resolved noise. Here, we probe the response of color centers in diamond in the presence of externally engineered random magnetic signals, and implement a deep neural network to methodically extract information on their associated spectral densities. Building on a long sequence of successive measurements under different types of stimuli, we show that our network manages to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field with good fidelity under a broad set of conditions and with only a minimal measured data set, even in the presence of substantial experimental noise. These proof-of-principle results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging.