Apparatus for producing a $$^{168}\hbox {Er}$$ Bose–Einstein condensate

Seo, Bojeong; Chen, Ziting; Huang, Mingchen; Parit, Mithilesh K.; He, Yue; Chen, Peng; Jo, Gyu-Boong

doi:10.1007/s40042-023-00767-0

Cited by 5 publications

(2 citation statements)

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“…In this study, we evaluate the performance of the neural network under various magnetic fields created by the coils B control , ranging from 0.6 G to 100 G. These values cover the typical range of magnetic fields used in experiments involving ultracold dipolar atoms. [21] This can be achieved by generating multiple prediction data sets with different average magnitudes of output. Before commencing the test, it is crucial to consider the average magnitude of the output of the training data since it could significantly affect the training process that determines the network's response to different magnetic fields.…”

Section: Nn's Performance Over a Range Of Magnetic Fieldmentioning

confidence: 99%

“…Since the target position inside the vacuum chamber is typically inaccessible, we detect magnetic fields at several surrounding positions, which are sent to the trained NN that is able to accurately deduce the magnetic field inside the vacuum chamber. We apply this method to our apparatus of erbium quantum gas, [20][21][22] which has a large magnetic dipole moment making it particularly sensitive to magnetic field vector. [23,24] We present the details of the NN-based method, including setting up the simulation model, training process, and final performance.…”

Section: Introductionmentioning

confidence: 99%

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Magnetic field regression using artificial neural networks for cold atom experiments

Chen 陈,

Wong 黃,

Seo

et al. 2024

Chinese Phys. B

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Accurately measuring magnetic fields is essential for magnetic-field sensitive experiments in areas like atomic, molecular, and optical physics, condensed matter experiments, and other areas. However, since many experiments are often conducted in an isolated environment that is inaccessible to experimentalists, it can be challenging to accurately determine the magnetic field at the target location. Here, we propose an efficient method for detecting magnetic fields with the assistance of an artificial neural network (NN). Instead of measuring the magnetic field directly at the desired location, we detect fields at several surrounding positions, and a trained NN can accurately predict the magnetic field at the target location. After training, we achieve a below 0.3% relative prediction error of magnetic field magnitude at the center of vacuum chamber, and successfully apply this method to our erbium quantum gas apparatus for accurate calibration of magnetic field and long-term monitor of environmental stray magnetic field. The demonstrated approach significantly simplifies the process of determining magnetic fields in isolated environments and can be applied to various research fields across a wide range of magnetic field magnitudes.

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Section: Nn's Performance Over a Range Of Magnetic Fieldmentioning

confidence: 99%