论文标题
基于深度学习的原子玻色 - 因斯坦冷凝水中的量子涡流检测
Deep learning based quantum vortex detection in atomic Bose-Einstein condensates
论文作者
论文摘要
量子涡流自然出现在旋转的玻色网凝结物（BEC）中，并且与它们的经典同行类似，允许研究一系列有趣的不平衡现象，例如湍流和混乱。但是，对这种现象的研究需要确定BEC中每个涡流的精确位置，当仅可用冷凝物密度或存在噪声源时，这变得具有挑战性，就像实验设置中通常情况一样。在这里，我们介绍了一个基于机器学习的涡流检测器，该检测器是由最新的对象检测方法激励的，该方法可以准确地定位在模拟的BEC密度图像中。我们的模型允许在嘈杂和非平衡配置中进行稳健和实时检测。此外，如果还可以使用冷凝水相位轮廓，则网络可以区分涡旋和抗涡流。我们预计，我们的涡旋检测器对于BEC中涡旋配置的静态和动力学特性的实验和理论研究都将是有利的。
Quantum vortices naturally emerge in rotating Bose-Einstein condensates (BECs) and, similarly to their classical counterparts, allow the study of a range of interesting out-of-equilibrium phenomena like turbulence and chaos. However, the study of such phenomena requires to determine the precise location of each vortex within a BEC, which becomes challenging when either only the condensate density is available or sources of noise are present, as is typically the case in experimental settings. Here, we introduce a machine learning based vortex detector motivated by state-of-the-art object detection methods that can accurately locate vortices in simulated BEC density images. Our model allows for robust and real-time detection in noisy and non-equilibrium configurations. Furthermore, the network can distinguish between vortices and anti-vortices if the condensate phase profile is also available. We anticipate that our vortex detector will be advantageous both for experimental and theoretical studies of the static and dynamical properties of vortex configurations in BECs.
