Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks

2026/03/31 by Devin Seay, Ishara P. Fernando, Dustin Keller
Physics and Astronomy · #physics.ins-det #hep-ph

paper · pdf · doi:10.1140/epja/s10050-026-01937-x

published as Eur. Phys. J. A 62, 153 (2026)

arxiv created 2026/08/03 · arxiv updated 2026/08/04

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.

Citations

• A New cw-NMR Q-meter for Dynamically Polarized Targets for Particle Physics
• An implementation of neural simulation-based inference for parameter estimation in ATLAS
• DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
• Image Data Augmentation Approaches: A Comprehensive Survey and Future directions
• Simulator-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms and Posterior Estimators for Inverse Problems
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Optuna: A Next-generation Hyperparameter Optimization Framework
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Squeeze-and-Excitation Networks
• Attention Is All You Need
• SGDR: Stochastic Gradient Descent with Warm Restarts
• Deep Residual Learning for Image Recognition
• Going Deeper with Convolutions
• Precision deuteron NMR signal measurement with the NA47 polarized target
• Multilayer feedforward networks are universal approximators
• Outline of a Theory of Statistical Estimation Based on the Classical Theory of Probability