Speaker
Description
Continuous wave Nuclear Magnetic Resonance (NMR) with constant current
has been pivotal in solid-state polarized target experiments within Nuclear and
High Energy Particle physics. Phase-sensitive detection using a Liverpool Q-
meter is conventionally employed for monitoring polarization during scattering
experiments. Yet, when operating outside of designed operational parameters,
there are significant nonlinearities have not yet been well understood for high-
fidelity running. Additionally under experimental conditions low signal to noise
can lead to much larger experimental uncertainties reducing the overall figure of
merit of the scattering experiments. This presentation discusses recent advance-
ments aimed at enhancing data acquisitions in NMR-based polarization mea-
surements and extending the operational capabilities of the Q-meter beyond its
designated parameters using machine learning (ML) to analyze measurements
with a low signal-to-noise ratio (SNR), corresponding to high noise levels. This
innovative approach enables more effective real-time online polarization mon-
itoring and offline data analysis, thereby enhancing the overall performance
metrics in scattering experiments involving Spin-1 target material.
