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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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能量分辨 $ \Delta E_{{0}}{/{\rm{eV}}} $ $ \Delta E_{{1}}{/{\rm{eV}}} $ $ \Delta E_{2}{/{\rm{eV}}} $ $ \Delta E_{3}{/{\rm{eV}}} $ $ \Delta E_{4}{/{\rm{eV}}} $ 衰减17 dB
的信号0.1015 0.3526 0.4360 0.4691 0.6140 衰减20 dB
的信号0.0955 0.3200 0.4199 0.4758 — 
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能量分辨 $ \Delta E_{{0}}{/{\rm{eV}}} $ $ \Delta E_{{1}}{/{\rm{eV}}} $ $ \Delta E_{2}{/{\rm{eV}}} $ $ \Delta E_{3}{/{\rm{eV}}} $ $ \Delta E_{4}{/{\rm{eV}}} $ $ \Delta E_{5}{/{\rm{eV}}} $ 实测噪声 0.1015 0.3526 0.4360 0.4691 0.6140 — 白噪声模型 0.1489 0.2992 0.3772 0.4382 0.4448 0.5113 提高 –31.83% +17.85% +15.59% +7.05% +38.04% — DownLoad:
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能量分辨 $ \Delta E_{{0}}{/{\rm{eV}}} $ $ \Delta E_{{1}}{/{\rm{eV}}} $ $ \Delta E_{2}{/{\rm{eV}}} $ $ \Delta E_{3}{/{\rm{eV}}} $ 实测噪声 0.0955 0.3200 0.4199 0.4758 白噪声模型 0.1553 0.2650 0.3748 0.4032 提高/% –62.61 +17.19 +10.74 +15.26 DownLoad:
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能量分辨 $ \Delta {{E}}_{{0}}{/{\rm{eV}}} $ $ \Delta {{E}}_{{1}}{/{\rm{eV}}} $ $ \Delta {{E}}_{2}{/{\rm{eV}}} $ $ \Delta {{E}}_{3}{/{\rm{eV}}} $ $ \Delta {{E}}_{4}{/{\rm{eV}}} $ $ \Delta {{E}}_{5}{/{\rm{eV}}} $ 实测噪声 0.1015 0.3526 0.4360 0.4691 0.6140 — 噪声白化 0.1469 0.3274 0.4263 0.4897 0.5009 0.6216 提高/% –44.73 +7.15 +2.22 –4.39 +18.42 — DownLoad:
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能量分辨 $ \Delta {{E}}_{{0}}{/{\rm{eV}}} $ $ \Delta {{E}}_{{1}}{/{\rm{eV}}} $ $ \Delta {{E}}_{2}{/{\rm{eV}}} $ $ \Delta {{E}}_{3}{/{\rm{eV}}} $ 实测噪声 0.0955 0.3200 0.4199 0.4758 噪声白化 0.1932 0.2855 0.39649 0.4196 提高/% –100.02 +10.78 +5.60 +11.81 DownLoad:
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能量分辨 $ \Delta {{E}}_{1}{/{\rm{eV}}} $ $ \Delta {{E}}_{{2}}{/{\rm{eV}}} $ $ \Delta {{E}}_{3}{/{\rm{eV}}} $ 实测噪声 0.3200 0.4199 0.4758 白噪声模型 0.2650
(+17.19%)0.3748
(+10.74%)0.4032
(+15.26%)噪声白化 0.2855
(+10.78%)0.39649
(+5.60%)0.4196
(+11.81%)DownLoad:
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[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25]
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