Optimal and efficient inference tools for field tracking with precessing spins

• URL: http://arxiv.org/abs/2510.11884v1
• Date: Mon, 13 Oct 2025 19:44:33 GMT
• Title: Optimal and efficient inference tools for field tracking with precessing spins
• Authors: Klaudia Dilcher, Piotr Bania, Diana Mendez-Avalos, Aleksandra Sierant, Morgan W. Mitchell, Jan Kolodynski,
• Abstract summary: Spin-precession magnetometer (SPM) observes electron, nucleus, color center, or muon spins as they precess in response to their local magnetic field.<n>We show that it is sufficient to accurately track fluctuating and unknown transient signals.<n>Our methods can be easily adapted to other types of sensors undergoing nonlinear dissipative dynamics.
• Score: 35.18016233072556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Precise, real-time monitoring of magnetic field evolution is important in applications including magnetic navigation and searches for physics beyond the standard model. One main field-monitoring technique, the spin-precession magnetometer (SPM), observes electron, nucleus, color center, or muon spins as they precess in response to their local magnetic field. Here, we study Bayesian signal-recovery methods for SPMs in the free-induction decay (FID) mode. In particular, we study tracking of field changes well within the coherence time of the spin system, and thus well beyond the response bandwidth, as in [Phys. Rev. Lett. 120, 040503 (2018)]. We derive the Bayesian Cram\'{e}r-Rao bound that dictates the ultimate precision in estimating the Larmor frequency, which we show to be attained by the computationally-expensive prediction error method (PEM). Relative to this benchmark, we show that the extended Kalman filter (EKF) and cubature Kalman filter (CKF) offer near-optimal tracking that is also computationally efficient, with the use of the latter giving better results only for large spin number. Focusing thus on the EKF, we show that it is sufficient to accurately track fluctuating and unknown transient signals. Our methods can be easily adapted to other types of sensors undergoing non-linear dissipative dynamics and experiencing intrinsic Gaussian-like stochastic noises.

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