Application of kernel principal component analysis for optical vector atomic magnetometry

Application of kernel principal component analysis for optical vector atomic magnetometry

Vector atomic magnetometers that incorporate electromagnetically induced transparency (EIT) allow for precision measurements of magnetic fields that are sensitive to the directionality of the observed field by virtue of fundamental physics. However, a practical methodology of accurately recovering t...

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Bibliographic Details
Main Authors: James A McKelvy, Irina Novikova, Eugeniy E Mikhailov, Mario A Maldonado, Isaac Fan, Yang Li, Ying-Ju Wang, John Kitching, Andrey B Matsko
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:Machine Learning: Science and Technology
Subjects:
kernel principal component analysis
vector magnetometry
unsupervised machine learning
support vector regression machines
Online Access:https://doi.org/10.1088/2632-2153/ad0fa4

Internet

https://doi.org/10.1088/2632-2153/ad0fa4

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