Systematic and statistical uncertainties of the hilbert-transform based high-precision FID frequency extraction method

Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision magnetic field measurements. The absolute value of the magnetic field is determined from the precession frequency of nuclear magnetic moments. The Hilbert transform is one of the methods that have been used to extract the phase function from the observed free induction decay (FID) signal and then its frequency. In this paper, a detailed implementation of a Hilbert-transform based FID frequency extraction method is described, and it is briefly compared with other commonly used frequency extraction methods. How artifacts and noise level in the FID signal affect the extracted phase function are derived analytically. A method of mitigating the artifacts in the extracted phase function of an FID is discussed. Correlations between noises of the phase function samples are studied for different noise spectra. We discovered that the error covariance matrix for the extracted phase function is nearly singular and improper for constructing the chi(2) used in the fitting routine. A down-sampling method for fixing the singular covariance matrix has been developed, so that the minimum chi(2)-fit yields properly the statistical uncertainty of the extracted frequency. Other practical methods of obtaining the statistical uncertainty are also discussed. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper presents a Hilbert-transform based FID frequency extraction method for high-precision magnetic field measurements using pulsed nuclear magnetic resonance (NMR). The method is compared with other commonly used frequency extraction methods, and the impact of artifacts and noise on the extracted phase function is analyzed. A down-sampling method is developed to address the nearly singular covariance matrix issue in order to properly determine the statistical uncertainty of the extracted frequency. Other practical methods for obtaining statistical uncertainty are also discussed.