Blue-detuned molecular magneto-optical trap schemes based on Bayesian optimization

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

  • S. Xu
  • R. Li
  • Y. Xia
  • M. Siercke
  • S. Ospelkaus

Externe Organisationen

  • National Institute of Extremely-Weak Magnetic Field Infrastructure
  • East China Normal University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Aufsatznummer033102
FachzeitschriftPhysical Review A
Jahrgang108
Ausgabenummer3
PublikationsstatusVeröffentlicht - 5 Sept. 2023

Abstract

Direct laser cooling and trapping of molecules to temperature below the Doppler limit and density exceeding 108 are challenging due to the sub-Doppler heating effects of a molecular magneto-optical trap (MOT). In our previous paper [S. Xu, Phys. Rev. Res. 4, L042036 (2022)2643-156410.1103/PhysRevResearch.4.L042036], we presented a general approach to engineering the sub-Doppler force by tuning the AC Stark shift with the addition of a blue-detuned laser. Here, by employing the Bayesian optimization method to optical Bloch equations, we have identified multiple blue-detuned MOT schemes for the CaF molecule. From the three-dimensional Monte Carlo simulation, we obtained a MOT temperature and density of 14 μK and 4.5×108 cm-3, respectively. Our findings present a potential avenue for directly loading molecular MOTs into conservative traps, which can capitalize on the high density and low temperature of the MOTs.

ASJC Scopus Sachgebiete

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Blue-detuned molecular magneto-optical trap schemes based on Bayesian optimization. / Xu, S.; Li, R.; Xia, Y. et al.
in: Physical Review A, Jahrgang 108, Nr. 3, 033102, 05.09.2023.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Xu S, Li R, Xia Y, Siercke M, Ospelkaus S. Blue-detuned molecular magneto-optical trap schemes based on Bayesian optimization. Physical Review A. 2023 Sep 5;108(3):033102. doi: 10.48550/arXiv.2305.16576, 10.1103/PhysRevA.108.033102
Xu, S. ; Li, R. ; Xia, Y. et al. / Blue-detuned molecular magneto-optical trap schemes based on Bayesian optimization. in: Physical Review A. 2023 ; Jahrgang 108, Nr. 3.
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AU - Siercke, M.

AU - Ospelkaus, S.

N1 - Funding Information: This work was supported by the National Natural Science Foundation of China under Grants No. 11834003 and No. 91836103. M. Siercke and S. Ospelkaus gratefully acknowledge financial support through Germany's Excellence Strategy—EXC-2123/1 QuantumFrontiers.

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