Maximized atom number for a grating magneto-optical trap via machine-learning assisted parameter optimization

We present a parameter set for obtaining the maximum number of atoms in a grating magneto-optical trap (gMOT) by employing a machine learning algorithm. In the multi-dimensional parameter space, which imposes a challenge for global optimization, the atom number is efficiently modeled via Bayesian op...

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Published in:Optics express 2021-10, Vol.29 (22), p.35623-35639
Main Authors: Seo, Sangwon, Lee, Jae Hoon, Lee, Sang-Bum, Park, Sang Eon, Seo, Meung Ho, Park, Jongcheol, Kwon, Taeg Yong, Hong, Hyun-Gue
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container_end_page 35639
container_issue 22
container_start_page 35623
container_title Optics express
container_volume 29
creator Seo, Sangwon
Lee, Jae Hoon
Lee, Sang-Bum
Park, Sang Eon
Seo, Meung Ho
Park, Jongcheol
Kwon, Taeg Yong
Hong, Hyun-Gue
description We present a parameter set for obtaining the maximum number of atoms in a grating magneto-optical trap (gMOT) by employing a machine learning algorithm. In the multi-dimensional parameter space, which imposes a challenge for global optimization, the atom number is efficiently modeled via Bayesian optimization with the evaluation of the trap performance given by a Monte-Carlo simulation. Modeling gMOTs for six representative atomic species - 7 Li, 23 Na, 87 Rb, 88 Sr, 133 Cs, 174 Yb - allows us to discover that the optimal grating reflectivity is consistently higher than a simple estimation based on balanced optical molasses. Our algorithm also yields the optimal diffraction angle which is independent of the beam waist. The validity of the optimal parameter set for the case of 87 Rb is experimentally verified using a set of grating chips with different reflectivities and diffraction angles.
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title Maximized atom number for a grating magneto-optical trap via machine-learning assisted parameter optimization
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