快速、准确且具有预测性的微陷阱阵列位分辨图像中原子检测方法
Fast, accurate, and predictive method for atom detection in site-resolved images of microtrap arrays
AI总结：
本文提出了一种基于估计理论的快速准确方法，用于微陷阱阵列位分辨图像中单个原子的检测，并通过实验验证其在高精度和实时应用中的有效性。
摘要：
我们介绍了一种基于估计理论的新方法，用于在微陷阱阵列的位分辨图像中检测单个原子，例如光学晶格或光学镊子阵列。利用标记测试图像，我们展示了在站点间距与点扩散函数半径相当时，与基于维纳去卷积的流行方法相比，检测准确性有显著提高。我们的方法的运行时间与站点数量近似线性相关，并且在100x100站点的桌面计算机上仍低于100毫秒。因此，它完全适用于实时使用。最后，我们提出了该问题的信号噪声比的严格定义，并证明它可以用于预测检测错误率。我们的工作为未来更大规模阵列或更小站点间距的实验打开了前景。
英文摘要：
We introduce a new method, rooted in estimation theory, to detect individual atoms in site-resolved images of microtrap arrays, such as optical lattices or optical tweezers arrays. Using labelled test images, we demonstrate drastic improvement of the detection accuracy compared to the popular method based on Wiener deconvolution when the inter-site distance is comparable to the radius of the point spread function. The runtime of our method scales approximately linearly with the number of sites, and remains well below 100 ms for an array of 100 x 100 sites on a desktop computer. It is therefore fully compatible with a real-time usage. Finally, we propose a rigorous definition for the signal-to-noise ratio of the problem, and show that it can be used as a predictor for the detection error rate. Our work opens the prospect for future experiments with increased array sizes, or reduced inter-site distances.
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