Spin Echo Simulations and Supervised Machine Learning
ORAL
Abstract
Using a numerical simulation of a phenomenological model of nuclear spin-spin interaction mediated by collective electronic spin excitations, we study the effects of such an interaction on nuclear magnetic resonance spin echo experiments. These local interactions can result in asymmetric spin echoes and unique time domain signals that are related to, and dependent on, the various parameters of the interaction. Simulating a large number of these echoes for different interaction parameters enables the use of supervised machine learning to analyze and “reverse engineer” the parameters of the interaction, demonstrating that the approach can be used to extract information about the form of the local interaction from the time domain spin echo signal.
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Presenters
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Charles Snider
Department of Physics, Brown University, Physics, Brown University
Authors
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Charles Snider
Department of Physics, Brown University, Physics, Brown University
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Stephen Carr
Harvard University, Brown University, Department of Physics, Brown University, Physics, Brown University
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Dmitri Feldman
Department of Physics, Brown University, Brown University, Physics, Brown University
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Chandrasekhar Ramanathan
Dartmouth College, Physics and Astronomy, Dartmouth College
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Brad Bradley Marston
Brown University, Department of Physics, Brown University, Physics, Brown University
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Vesna Mitrovic
Brown University, Department of Physics, Brown University, Physics, Brown University