Bulletin of the American Physical Society
53rd Annual Meeting of the APS Division of Atomic, Molecular and Optical Physics
Volume 67, Number 7
Monday–Friday, May 30–June 3 2022; Orlando, Florida
Session V01: Poster Session III (4:00-6:00pm, EDT)
4:00 PM,
Thursday, June 2, 2022
Room: Grand Ballroom C
Abstract: V01.00035 : Optimization and Stabilization of Cooling Processes of Neutral Atoms with Machine Learning
Presenter:
Nicholas Milson
Authors:
Nicholas Milson
Arina Tashchilina
(University of Alberta)
Logan W Cooke
(Univ of Alberta)
Joseph Lindon
(Univ of Alberta)
Anna Prus-Czarnecka
(University of Alberta)
Lindsay J LeBlanc
(Alberta)
This is a high-dimensional problem, to which machine learning lends itself well. A neural network is used to model the atom number at various stages of our atom cooling system as a function of several parameters measured around the laboratory. Standard methods in neural network optimization improve computation time, and accuracy of out-of-sample predictions. Dimensionality reduction, adaptive learning rates when optimizing the model, and regularization by dropping connections of neurons yielded robust and consistent predictions.
Using this approach, we develop a neural-network model that successfully predicts our atom number in experiments, and we are working towards using this information to reduce instabilities in the atom number.
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