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中国物理学会期刊

超导动态电感单光子探测器的噪声处理

CSTR: 32037.14.aps.70.20210185

Noise processing of superconducting kinetic inductance single photon detector

CSTR: 32037.14.aps.70.20210185
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  • 噪声是影响弱信号检测器件性能指标的主要因素之一, 而最优滤波算法是白噪声背景中自适应提取弱有用信号的一种常见处理方法. 本文针对极低温环境下微波动态电感探测器(microwave kinetic inductance detector, MKID)光子弱信号响应的噪声特性, 在改进噪声模型的基础上利用最优滤波算法改进了探测信号的噪声处理. 结果表明, 经过改进噪声模型的算法处理, MKID的能量分辨(单光子探测器的主要性能指标之一)得到了15%左右的提升, 实现了0.26 eV的红外单光子能量分辨.

     

    Noise is one of the main factors affecting the performance index of weak signal detection devices, and the optimal filtering algorithm is an effective method to adaptively extract various useful weak signals from the white noise background. In order to improve the performance of single photon detector (especially the photon number resolution ability), one mainly focuses on the optimization of detector hardware such as the optimization of photosensitive materials and the technology of device fabrication. However, in this paper the performance of microwave kinetic Inductance detector (MKID) in the way of data processing is improved. Considering the fact that the template of light pulse signal in the optimal filtering algorithm is obtained by taking the average, we replace the noise model in the original optimal filtering algorithm with the white noise model and the whitening noise model. Then we process the photon response data that are detected by the MKID in an extremely low temperature environment. The results show that the energy resolution (one of the main performance indexes of single photon detector) of MKID is improved by about 15%, and we achieve an infrared single photon energy resolution of 0.26 eV. In this paper, the application and development trends of superconducting single photon detector are briefed. Then, how the MKID responds to weak coherent optical signal in low temperature environment, and the process of signal conversion, acquisition and output are explained in detail. According to the optimal filtering algorithm, we use different noise models to analyze the results of the signals detected by MKID. After that, we count the optimal amplitude multiple, perform the Gaussian fitting analysis on the statistical graph, and compare the energy resolution with the photon number resolution of the optimal filtering algorithm under different noise models. As a result, we find that under the white noise model, the optimal filtering algorithm is used to obtain the best result for MKID processing, and high energy resolution can be achieved.

     

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