Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks

TL;DR

This paper introduces neural network techniques to enhance continuous-wave NMR polarization measurements, significantly reducing uncertainties and improving real-time monitoring in high-energy physics experiments.

Contribution

It is the first application of deep neural networks to CW-NMR polarization metrology, improving accuracy and robustness of polarization measurements.

Findings

Neural networks reduce fitting uncertainties in NMR signals.

Enhanced real-time polarization monitoring achieved.

Method improves offline analysis precision.

Abstract

Continuous-wave Nuclear Magnetic Resonance (CW-NMR) operated in constant-current mode has served as a foundational technique for polarization measurement in solid-state dynamically polarized targets within nuclear and high-energy physics experiments for several decades, and it remains an essential tool. Conventional Q-meter-based phase-sensitive detection is critical for precise real-time determination of target polarization during scattering runs. However, the accuracy and reliability of these measurements are frequently compromised by elevated noise levels, baseline drift, and systematic uncertainties arising from signal isolation and fitting, ultimately degrading the overall experimental figure of merit. In this work, we report the first successful application of neural network architectures to continuous-wave NMR polarization metrology. By leveraging advanced machine learning techniques for signal extraction and denoising, we achieve a substantial reduction of fitting uncertainties under a variety of realistic simulated and experimental conditions. These improvements translate directly into more robust real-time (online) polarization monitoring and higher precision in subsequent offline analysis. By reducing analysis-induced uncertainty, the resulting methodology can improve the effective figure of merit for scattering experiments employing dynamically polarized targets and provides a new toolset for NMR-based polarimetry in high-energy and nuclear physics.