论文标题
用于实现Thulium原子的Bose-Einstein凝结的机器学习
Machine Learning for Achieving Bose-Einstein Condensation of Thulium Atoms
论文作者
论文摘要
Bose-Einstein冷凝（BEC）是多种研究活动的强大工具，其中很大一部分与量子模拟有关。各种问题可能会受益于不同的原子种类，但是冷却新的物种以使量子模拟以使温度需要大量优化，并且通常被视为一项艰巨的实验任务。在这项工作中，我们实施了贝叶斯机器学习技术，以优化Thulium原子的蒸发冷却，并在532 nm附近运行的光学偶极陷阱中实现BEC。开发的方法可用于冷却其他新型的原子种类以量子退化，而无需对其性质进行其他研究。
Bose-Einstein condensation (BEC) is a powerful tool for a wide range of research activities, a large fraction of which are related to quantum simulations. Various problems may benefit from different atomic species, but cooling down novel species interesting for quantum simulations to BEC temperatures requires a substantial amount of optimization and is usually considered as a hard experimental task. In this work, we implemented the Bayesian machine learning technique to optimize the evaporative cooling of thulium atoms and achieved BEC in an optical dipole trap operating near 532 nm. The developed approach could be used to cool down other novel atomic species to quantum degeneracy without additional studies of their properties.
