Neural networks trained on realistic Q-meter simulations reduce CW-NMR polarization extraction uncertainties under noise and baseline drift relative to conventional TE-area and Dulya lineshape fits.
A line-shape analysis for spin-1 NMR signals
2 Pith papers cite this work. Polarity classification is still indexing.
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AFP efficiencies are measured across ammonia and butanol targets, and a joint lineshape fit extracts vector and tensor polarizations from non-Boltzmann deuteron AFP spectra.
citing papers explorer
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Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks
Neural networks trained on realistic Q-meter simulations reduce CW-NMR polarization extraction uncertainties under noise and baseline drift relative to conventional TE-area and Dulya lineshape fits.
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Adiabatic Fast Passage Spin Manipulation Measurements in Solid Polarized Targets
AFP efficiencies are measured across ammonia and butanol targets, and a joint lineshape fit extracts vector and tensor polarizations from non-Boltzmann deuteron AFP spectra.