Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1304.7066 · Chinese Physics B

Systematically investigating the polarization gradient cooling in an optical molasses of ultracold cesium atoms

Zhonghua Ji, Jinpeng Yuan, Yanting Zhao, Xue-Fang Chang, Liantuan Xiao, Suotang Jia

Published Nov 1, 2014• 9 citations
arXivHTMLPDF

Abstract

We systematically investigate the dependence of the temperature of cold cesium atoms of polarization gradient cooling (PGC) in optical molasses on experimental parameters, which contain changing modes of cooling laser, PGC interaction time, cooling laser frequency and its intensity. The SR mode of cooling laser, that means the cooling laser frequency is changed with step mode and cooling laser intensity is changed with ramp mode, is found to be the best for PGC comparing with other SS, RS, and RR modes. We introduce a statistical explanation and an exponential decay function to explain the variation of cold atomic temperature on time. The heating effect is observed when the cooling laser intensity is lower than the saturation intensity of cold atoms. After optimization, the lowest temperature of cold cesium atoms is observed to be about 4uK with the number of 2x10^9, a density of 1x10^11/cm^3 and the phase space density of 4.4x10^(-5). The optimization process and analysis of controllable experimental parameters are also meaningful for other cold atomic systems.

DOI
10.1088/1674-1056/23/11/113702

Related papers

Optimization and analysis of experimental parameters for polarization gradient cooling of cold cesium atoms

Zhonghua Ji, Jinpeng Yuan, Yanting Zhao +4 more

We systematically investigate the dependence of the temperature of cold cesium atoms of polarization gradient cooling (PGC) in optical molasses on experimental parameters, which contain changing modes of cooling laser, PGC interaction time, cooling laser frequency and its intensity. The SR mode of cooling laser, that means the cooling laser frequency is changed with step mode and cooling laser intensity is changed with ramp mode, is found to be the best for PGC comparing with other SS, RS, and RR modes. We introduce a statistical explanation and an exponential decay function to explain the variation of cold atomic temperature on time. The heating effect is observed when the cooling laser intensity is lower than the saturation intensity of cold atoms. After optimization, the lowest temperature of cold cesium atoms is observed to be about 4uK with the number of 2x10^9, a density of 1x10^11/cm^3 and the phase space density of 4.4x10^(-5). The optimization process and analysis of controllable experimental parameters are also meaningful for other cold atomic systems.

•Apr 26, 2013•0 citations

Systematically investigating the polarization gradient cooling in an optical molasses of ultracold cesium atoms

姬中华, 元晋鹏, 赵延霆 +3 more

We systematically investigate the polarization gradient cooling(PGC) process in an optical molasses of ultracold cesium atoms. The SR mode for changing the cooling laser, which means that the cooling laser frequency is stepped to the setting value while its intensity is ramped, is found to be the best for the PGC, compared with other modes studied. We verify that the heating effect of the cold atoms, which appears when the cooling laser intensity is lower than the saturation intensity, arises from insufficient polarization gradient cooling. Finally, an exponential decay function with a statistical explanation is introduced to explain the dependence of the cold atom temperature on the PGC interaction time.

中国物理B：英文版•Jan 1, 2014•0 citations

Experimental study on laser cooling of cesium atoms by optical molasses

Jidong Hou, LI Yi-min, Donghai Yang +1 more

We have studied the relationship between the temperature of Cs cold atoms and the parameters of the cooling laser. The atoms are first cooled and captured by a magneto-optical trap and then further cooled by optical molasses. The temperature of atoms is measured by a time-of-flight method. For relatively large laser intensity and small detuning, the temperature depends linearly on the intensity-to-detuning ratio, and nonlinear relationship has also been found in other case.

Acta Physica Sinica (Overseas Edition)•Dec 1, 1998•2 citations

Bose-Einstein Condensation by Polarization Gradient Laser Cooling

Wenchao Xu, Tamara Šumarac, Emily H. Qiu +7 more

Attempts to create quantum degenerate gases without evaporative cooling have been pursued since the early days of laser cooling, with the consensus that polarization gradient cooling (PGC, also known as "optical molasses") alone cannot reach condensation. In the present work, we report that simple PGC can generate a small Bose-Einstein condensate (BEC) inside a corrugated micrometer-sized optical dipole trap. The experimental parameters enabling BEC creation were found by machine learning, which increased the atom number by a factor of 5 and decreased the temperature by a factor of 2.5, corresponding to almost 2 orders of magnitude gain in phase space density. When the trapping light is slightly misaligned through a microscopic objective lens, a BEC of ∼250 ^{87}Rb atoms is formed inside a local dimple within 40 ms of PGC after MOT loading.

arXiv:2312.07708•Physical Review Letters•Jun 4, 2024•4 citations

Multi-parameter optimization of polarization gradient cooling for ⁸⁷Rb atoms based on reinforcement learning

Changwen Liang, Shaojun Gao, Jixun Liu +5 more

Polarization gradient cooling (PGC) plays an important role in many cold atom applications including the formation of Bose-Einstein condensates (BECs) and cooling of single atom. Traditional parameter optimization of PGC usually relies on subjective expertise, faces challenges in fine manipulation, and exhibits low optimization efficiency. Here, we propose a segmented control method that differs from traditional PGC process by expanding the experiment parameters from 3 to 30. Subsequently, the conventional timing optimization problem is reformulated as a Markov Decision Process (MDP), and the experiment parameters are optimized using a reinforcement learning model. With proper settings of hyperparameters, the learning process exhibits good convergence and powerful parameter exploration capabilities. Finally, we capture ~4.3×10⁸ cold atoms, with a phase space density of ~7.1×10^(-4) at a temperature of ~3.7 μK in ~14.6 minutes. Our work paves the way for intelligent preparation of degenerate quantum gas.

•Dec 23, 2024•0 citations

Multi-parameter optimization of polarization gradient cooling for ⁸⁷Rb atoms based on reinforcement learning

Changwen Liang, Shaojun Gao, Jixun Liu +5 more

Polarization gradient cooling (PGC) plays an important role in many cold atom applications including the formation of Bose-Einstein condensates (BECs) and cooling of single atom. Traditional parameter optimization of PGC usually relies on subjective expertise, faces challenges in fine manipulation, and exhibits low optimization efficiency. Here, we propose a segmented control method that differs from traditional PGC process by expanding the experiment parameters from 3 to 30. Subsequently, the conventional timing optimization problem is reformulated as a Markov Decision Process (MDP), and the experiment parameters are optimized using a reinforcement learning model. With proper settings of hyperparameters, the learning process exhibits good convergence and powerful parameter exploration capabilities. Finally, we capture ~4.3×10⁸ cold atoms, with a phase space density of ~7.1×10^(-4) at a temperature of ~3.7 μK in ~14.6 minutes. Our work paves the way for intelligent preparation of degenerate quantum gas.

•Jul 31, 2024•0 citations
We systematically investigate the dependence of the temperature of cold cesium atoms of polarization gradient cooling (PGC) in optical molasses on experimental parameters, which contain changing modes of cooling laser, PGC interaction time, cooling laser frequency and its intensity. The SR mode of cooling laser, that means the cooling laser frequency is changed with step mode and cooling laser intensity is changed with ramp mode, is found to be the best for PGC comparing with other SS, RS, and RR modes. We introduce a statistical explanation and an exponential decay function to explain the variation of cold atomic temperature on time. The heating effect is observed when the cooling laser intensity is lower than the saturation intensity of cold atoms. After optimization, the lowest temperature of cold cesium atoms is observed to be about 4uK with the number of 2x10^9, a density of 1x10^11/cm^3 and the phase space density of 4.4x10^(-5). The optimization process and analysis of controllable experimental parameters are also meaningful for other cold atomic systems.
We systematically investigate the dependence of the temperature of cold cesium atoms of polarization gradient cooling (PGC) in optical molasses on experimental parameters, which contain changing modes of cooling laser, PGC interaction time, cooling laser frequency and its intensity. The SR mode of cooling laser, that means the cooling laser frequency is changed with step mode and cooling laser intensity is changed with ramp mode, is found to be the best for PGC comparing with other SS, RS, and RR modes. We introduce a statistical explanation and an exponential decay function to explain the variation of cold atomic temperature on time. The heating effect is observed when the cooling laser intensity is lower than the saturation intensity of cold atoms. After optimization, the lowest temperature of cold cesium atoms is observed to be about 4uK with the number of 2x10^9, a density of 1x10^11/cm^3 and the phase space density of 4.4x10^(-5). The optimization process and analysis of controllable experimental parameters are also meaningful for other cold atomic systems.
We systematically investigate the polarization gradient cooling(PGC) process in an optical molasses of ultracold cesium atoms. The SR mode for changing the cooling laser, which means that the cooling laser frequency is stepped to the setting value while its intensity is ramped, is found to be the best for the PGC, compared with other modes studied. We verify that the heating effect of the cold atoms, which appears when the cooling laser intensity is lower than the saturation intensity, arises from insufficient polarization gradient cooling. Finally, an exponential decay function with a statistical explanation is introduced to explain the dependence of the cold atom temperature on the PGC interaction time.
Attempts to create quantum degenerate gases without evaporative cooling have been pursued since the early days of laser cooling, with the consensus that polarization gradient cooling (PGC, also known as "optical molasses") alone cannot reach condensation. In the present work, we report that simple PGC can generate a small Bose-Einstein condensate (BEC) inside a corrugated micrometer-sized optical dipole trap. The experimental parameters enabling BEC creation were found by machine learning, which increased the atom number by a factor of 5 and decreased the temperature by a factor of 2.5, corresponding to almost 2 orders of magnitude gain in phase space density. When the trapping light is slightly misaligned through a microscopic objective lens, a BEC of ∼250 ^{87}Rb atoms is formed inside a local dimple within 40 ms of PGC after MOT loading.
Polarization gradient cooling (PGC) plays an important role in many cold atom applications including the formation of Bose-Einstein condensates (BECs) and cooling of single atom. Traditional parameter optimization of PGC usually relies on subjective expertise, faces challenges in fine manipulation, and exhibits low optimization efficiency. Here, we propose a segmented control method that differs from traditional PGC process by expanding the experiment parameters from 3 to 30. Subsequently, the conventional timing optimization problem is reformulated as a Markov Decision Process (MDP), and the experiment parameters are optimized using a reinforcement learning model. With proper settings of hyperparameters, the learning process exhibits good convergence and powerful parameter exploration capabilities. Finally, we capture ~4.3×10⁸ cold atoms, with a phase space density of ~7.1×10^(-4) at a temperature of ~3.7 μK in ~14.6 minutes. Our work paves the way for intelligent preparation of degenerate quantum gas.
Polarization gradient cooling (PGC) plays an important role in many cold atom applications including the formation of Bose-Einstein condensates (BECs) and cooling of single atom. Traditional parameter optimization of PGC usually relies on subjective expertise, faces challenges in fine manipulation, and exhibits low optimization efficiency. Here, we propose a segmented control method that differs from traditional PGC process by expanding the experiment parameters from 3 to 30. Subsequently, the conventional timing optimization problem is reformulated as a Markov Decision Process (MDP), and the experiment parameters are optimized using a reinforcement learning model. With proper settings of hyperparameters, the learning process exhibits good convergence and powerful parameter exploration capabilities. Finally, we capture ~4.3×10⁸ cold atoms, with a phase space density of ~7.1×10^(-4) at a temperature of ~3.7 μK in ~14.6 minutes. Our work paves the way for intelligent preparation of degenerate quantum gas.