A saturation-absorption rubidium magnetometer with multilevel optical Bloch-equation modeling for intermediate-to-high fields
Mayand Dangi, Prateek Rajan Gupta, Joseph Kasti, Nivedan Vishwanath, Michael Zepp, David Smith, Benedikt Geiger, Jennifer T. Choy
TL;DR
SASHMAG presents a rubidium-based magnetometer for intermediate-to-high magnetic fields ($0.2$–$0.4$ T) that operates in the hyperfine Paschen-Back regime. It combines saturated absorption spectroscopy in Faraday geometry with a multilevel optical Bloch-equation model solved in the uncoupled basis $|m_I,m_J angle$, plus a physics-constrained optimization to infer the magnetic field from sub-Doppler line centers. The approach is validated by reproducing power-broadened and Doppler-broadened spectral features and by achieving precise field estimates with MC-based uncertainty analysis, showing $ ext{B}$-estimates of $0.2602$–$0.4092$ T and a sensitivity of $0.42$ mT/$ ext{Hz}^{1/2}$. This framework enables high-field metrology with potential applications in MRI and fusion, and outlines a path toward autonomous sensing via synthetic data generated from the OBE model.
Abstract
We present SASHMAG (Saturated Absorption Spectroscopy High-field MAGnetometer), an atomic sensor designed for precision magnetic-field measurements in the intermediate-to-high field regime ($>0.2\,\text{T}$) using Rubidium-87 ($^{87}Rb$). The sensor operates in the hyperfine Paschen-Back regime, where the hyperfine and Zeeman interactions decouple, and utilizes counter-propagating pump-probe configuration in Faraday geometry to resolve isolated, Doppler-free Zeeman transitions. To interpret the resulting spectra in this strongly field-dependent regime, we developed a comprehensive multilevel optical Bloch-equation model solved explicitly in the uncoupled $\ket{m_I, m_J}$ basis, capturing state mixing and nonlinear saturation dynamics. This model reproduces measured spectra at sub-Doppler resolution and is consistent with analytical expectations for power broadening and thermal Doppler scaling. Magnetic field estimation is performed using a physics-constrained optimization routine that infers the magnetic field by minimizing the residual between experimentally extracted line centers and calculated transition frequencies from the field-dependent Hamiltonian. We demonstrate magnetic field retrieval from $0.2\,\text{T}$ to $0.4\,\text{T}$ with a precision of $\pm 0.0017 \,\text{T}$). Furthermore, the validated simulation establishes a foundation for generating synthetic training datasets, paving the way for autonomous, Machine Learning-enhanced magnetometry in applications ranging from MRI to fusion reactors.
