SPIE-CLP Conference on Advanced Photonics

Omnidirectional vector sensing with a single-beam magnetometer via deep learning

Feifan Yang, Gen Hu, Pengcheng Du

Published Mar 25, 2026• 0 citations

Abstract

Quantum magnetometry provides remarkable sensitivity and miniaturization potential for precision measurement, yet conventional vector magnetic field detection often suffers from hardware complexity and directional dead zones. Here we present a deep learning-based, single-beam all-optical vector magnetometer that enables end-to-end neural network modeling for omnidirectional magnetic field sensing without dead zones. By exploiting the nonlinear relationship between coherent population trapping (CPT) spectra of 87Rb D1 line and field orientation, we construct a one-dimensional deep residual network to directly decode single-pass spectral signals into azimuth angles, fully resolving the φ/φ+180° ambiguity inherent in traditional approaches. The experimental results demonstrate that the proposed scheme has a good effect on eliminating degeneracy problems, and the system performance is tested that pitch angular sensitivity of 0.01°/Hz1/2, and azimuth angular sensitivity of 0.01rad without additional modulation coils or multi-beam configurations. This work establishes a general framework for intelligent spectral inversion of complex physical quantities and offers a single-beam architecture suitable for highly integrated quantum sensors.

DOI: 10.1117/12.3100645

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