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首页> 外文期刊>Journal of the Optical Society of America, B. Optical Physics >Algorithm for finding clusters with a known distribution and its application to photon-number resolution using a superconducting transition-edge sensor
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Algorithm for finding clusters with a known distribution and its application to photon-number resolution using a superconducting transition-edge sensor

机译：查找具有已知分布的簇的算法及其在使用超导过渡边缘传感器的光子数解析中的应用

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摘要

Improving photon-number resolution of single-photon sensitive detectors is important for many applications, as is extending the range of such detectors. Here we seek improved resolution for a particular superconducting transition-edge sensor (TES) through better processing of the TES output waveforms. With that aim, two algorithms to extract number resolution from TES output waveforms are compared. The comparison is done by processing waveform data sets from a TES illuminated at nine illumination levels by a pulsed laser at 1550 nm. The algorithms are used to sort the individual output waveforms and then create clusters associated with individual photon numbers. The first uses a dot product with the waveform mean (for each illumination level), while the second uses K-means clustering modified to include knowledge of the Poisson distribution. The first algorithm is shown to distinguish adjacent peaks associated with photon numbers up to 19, whereas the second algorithm distinguishes photon numbers up to 23, using the same data.
机译：对于许多应用而言，提高单光子敏感探测器的光子数分辨率非常重要，因为扩大了此类探测器的范围。在这里，我们寻求通过更好地处理TES输出波形来提高特定超导过渡边缘传感器（TES）的分辨率。为此，比较了两种从TES输出波形中提取数字分辨率的算法。比较是通过处理来自1550 nm脉冲激光在9个照明水平下照明的TES的波形数据集来完成的。该算法用于对各个输出波形进行排序，然后创建与各个光子数关联的簇。第一种方法使用点积，其波形平均值（针对每个照明水平），而第二种方法则使用经过修改的K均值聚类以包含Poisson分布的知识。所示的第一种算法可以区分与最多19个光子数相关的相邻峰，而第二种算法可以使用相同的数据区分最多23个光子数。

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