Deep neural networks trained on simulated Q-meter NMR spectra can extract target polarization with lower fitting uncertainty than conventional least-squares lineshape fitting, at least when the test data come from the same simulator.
A line-shape analysis for spin-1 NMR signals
2 Pith papers cite this work, alongside 27 external citations. Polarity classification is still indexing.
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Pith papers citing it
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external citations · OpenAlex
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CONDITIONAL 2representative citing papers
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.
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Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks
Deep neural networks trained on simulated Q-meter NMR spectra can extract target polarization with lower fitting uncertainty than conventional least-squares lineshape fitting, at least when the test data come from the same simulator.
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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.