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Potential Due to a Polarized Object01:29

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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
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Atomic Nuclei: Nuclear Relaxation Processes01:23

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In the absence of an external magnetic field, nuclear spin states are degenerate and randomly oriented. When a magnetic field is applied, the spins begin to precess and orient themselves along (lower energy) or against (higher energy) the direction of the field. At equilibrium, a slight excess population of spins exists in the lower energy state. Because the direction of the magnetic field is fixed as the z-axis,  the precessing magnetic moments are randomly oriented around the z-axis.
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斯-爱因斯坦凝结通过极化梯度激光冷却冷凝.

Wenchao Xu1,2, Tamara Šumarac1,3, Emily H Qiu1

  • 1Department of Physics and Research Laboratory of Electronics, <a href="https://ror.org/042nb2s44">Massachusetts Institute of Technology</a>, Cambridge, Massachusetts 02139, USA.

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概括

简单的极化梯度冷却 (PGC) 现在可以在没有蒸发冷却的情况下创建波斯-爱因斯坦冷凝物 (BEC). 机器学习优化了参数,显著提高了BEC形成的相位密度.

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科学领域:

  • 量子物理学的量子物理学
  • 原子物理 原子物理
  • 激光冷却可以冷却.

背景情况:

  • 蒸发式冷却是实现斯-爱因斯坦凝结 (BEC) 的标准方法.
  • 极化梯度冷却 (PGC) 以前被认为仅用于BEC形成是不够的.

研究的目的:

  • 为了调查单独的PGC是否可以创建BEC.
  • 优化使用PGC创建BEC的实验参数.

主要方法:

  • 使用一个微米大小的波纹光学二极管陷.
  • 采用机器学习来找到最佳的实验参数.
  • 通过显微镜的客观镜头略有错位的捕捉光.

主要成果:

  • 仅使用PGC成功生成波斯-爱因斯坦凝聚物 (BEC).
  • 通过大约250 ^ {87} Rb原子实现了 BEC 的形成.
  • 在MOT加载后,在PGC的40ms内创建BEC.
  • 机器学习使原子数提高了5倍,温度降低了2.5倍,相空间密度增加了100倍.

结论:

  • 在优化条件下,简单的PGC足以创建BEC.
  • 机器学习是优化量子气体实验的强大工具.
  • 这项工作挑战了长期以来关于PGC限制的共识.