Mr. James McKelvy Profile

James McKelvy

at Jet Propulsion Lab

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Publications (2)

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Proceedings Article | 9 March 2023 Presentation

Vector atomic magnetometer based on EIT

Irina Novikova, Eugeniy Mikhailov, Mario Alberto González Maldonado, Alex Toytyla, Andrey Matsko, Jamie McKelvy, Isaac Fan, Yang Li, John Kitching

Proceedings Volume PC12447, PC124471X (2023) https://doi.org/10.1117/12.2657331

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Here, we describe progress toward realization of a vector magnetometer based on all-optical excitation of an atomic ensemble in a vapor cell under the conditions of electromagnetically induced transparency (EIT). The EIT resonance amplitudes depend on relative orientations of the three key vectors: laser wave-vector, polarization, and the direction of the magnetic field. By analyzing possible two-photon transitions and their combinations, we can, in principle, analytically calculate the amplitude of various EIT resonances as functions of the relative angles between the magnetic field, polarization and laser propagation vectors. By locking the polarization to one of these maxima, one can determine the plane formed by the magnetic field and the light wave vector to the accuracy better than 0.001 rad. Analysis of the relative resonance amplitude may be used to extract the full information about the magnetic field direction.

Proceedings Article | 3 March 2022 Presentation + Paper

Machine learning for deep space network antenna motions detection

Victor Li, James McKelvy, Lin Yi

Proceedings Volume 12019, 120190H (2022) https://doi.org/10.1117/12.2608321

KEYWORDS: Antennas, Data modeling, Machine learning, Fiber optics, Motion detection, Metrology

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Highly stable frequency and timing standards are essential for deep-space missions and radio science. At the NASA Deep Space Network (DSN), these standards are distributed through a network of underground fiber cables to support several Goldstone antennas. Independently developed frequency-measuring instruments generate tremendous quantities of data to monitor and validate the antennas’ stringent frequency requirements. In this paper, we propose a lightweight processing tool capable of detecting disturbances on the frequency signal caused by DSN antenna motions. Our training data is sampled from the movement log of the antenna of interest and the generated data from the fiber optic metrology instrument linked to the antenna. We demonstrate that a convolutional neural network (CNN) model can achieve high accuracies on classifying instances of antenna movements and is an effective predictor when used iteratively on longer, variable stretches of metrology data. The simplicity, low training cost, and high accuracies of our model strongly suggest its efficacy in identifying and troubleshooting frequency disturbances caused by the antenna.

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