发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出顺序截止检测方法，将自适应贝叶斯估计简化为时间序列比较，实现无需实时概率计算的最优荧光状态检测，并通过蒙特卡洛模拟和Ba$^+$实验验证。
AI 中文摘要
我们证明，在荧光状态检测中，自适应贝叶斯估计可简化为将经过时间与由亮计数率和暗计数率以及置信参数 $\epsilon_d$ 和 $\epsilon_b$ 确定的两个算术时间序列进行比较。实时计算中不涉及概率，由此产生的实现（我们称之为顺序截止检测）在平均检测时间上被证明是最优的。该分析允许对检测时间分布、平均检测时间、两个通道的误码率（考虑失准计数率）、检测期间状态之间的衰减以及单光子计数模块中的后脉冲效应进行闭式表达。这些预测通过蒙特卡洛模拟得到验证，并通过 Ba$^+$ 的荧光检测进行了演示。
英文摘要
We show that adaptive Bayesian estimation in fluorescence state detection reduces to a comparison of elapsed time with two arithmetic time sequences fixed by the bright and dark count rates and confidence parameters $ε_d$ and $ε_b$. No probabilities are computed in real time and the resulting implementation, which we term sequential deadline detection, is provably optimal in mean detection time. The analysis permits closed-form expressions for detection-time distributions, mean detection times, error rates of both channels accounting for miscalibrated rates, decay between the states during detection, and afterpulsing in single photon counting modules. The predictions are validated by Monte Carlo simulation and demonstrated by fluorescence detection with Ba$^+$
Comments5 pages, 3 figures, supplemental provides summary of mathematical derivations
