Bose-Einstein condensation (BEC) is a powerful tool for a wide range of research activities, a large fraction of which is 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 to be a difficult 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.

中文翻译：

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机器学习以实现B原子的玻色-爱因斯坦凝聚

玻色-爱因斯坦凝聚（BEC）是进行广泛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，但是将量子模拟感兴趣的新种类冷却到BEC温度需要大量优化，通常被认为是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来优化th原子的蒸发冷却，并在工作于532 nm附近的光学偶极阱中实现了BEC。无需进一步研究其性质，即可将开发的方法用于将其他新原子物种冷却至量子简并。