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Improvement of energy resolution of x-ray transition-edge sensor using K-means algorithm and Wiener filter |
| Qingxiao Ma(马卿效)1,2, Wen Zhang(张文)1,†, Peizhan Li(李佩展)1,2, Zheng Wang(王争)1, Zhifa Feng(冯志发)1,2, Xinkai Yang(杨心开)1,2, Jiaqiang Zhong(钟家强)1, Wei Miao(缪巍)1, Yuan Ren(任远)1, Jing Li(李婧)1, and Shengcai Shi(史生才)1 |
1 Purple Mountain Observatory, Chinese Academic of Sciences, Nanjing 210023, China;
2 College of Astronomy and Space Science, University of Science and Technology of China, Hefei 230026, China |
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Abstract We develop an x-ray Ti/Au transition-edge sensor (TES) with an Au absorber deposited on the center of TES and improved its energy resolution using the K-means clustering algorithm in combination with Wiener filter. We firstly extract the main parameters of each recorded pulse trace, which are adopted to classify these traces into several clusters in the K-means clustering algorithm. Then real traces are selected for energy resolution analysis. Following the baseline correction, the Wiener filter is used to improve the signal-to-noise ratio. Although the silicon underneath the TES has not been etched to reduce the thermal conductance, the energy resolution of the developed x-ray TES is improved from 94 eV to 44 eV at 5.9 keV.
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Received: 13 April 2023
Revised: 11 May 2023
Accepted manuscript online: 23 May 2023
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PACS:
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85.25.Oj
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(Superconducting optical, X-ray, and γ-ray detectors (SIS, NIS, transition edge))
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85.25.Am
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(Superconducting device characterization, design, and modeling)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 12293032, 120101002, 12173097, and U1931123), the National Key Basic Research and Development Program of China (Grant Nos. 2020YFC2201703 and 2018YFA0404701), and Chinese Academy of Sciences (Grant No. GJJSTD20210002). |
Corresponding Authors:
Wen Zhang
E-mail: wzhang@pmo.ac.cn
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Cite this article:
Qingxiao Ma(马卿效), Wen Zhang(张文), Peizhan Li(李佩展), Zheng Wang(王争), Zhifa Feng(冯志发), Xinkai Yang(杨心开), Jiaqiang Zhong(钟家强), Wei Miao(缪巍), Yuan Ren(任远), Jing Li(李婧), and Shengcai Shi(史生才) Improvement of energy resolution of x-ray transition-edge sensor using K-means algorithm and Wiener filter 2023 Chin. Phys. B 32 108501
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[1] Irwin K D and Hilton G C 2005 Cryogenic Particle Detection (Berlin: Springer) pp. 63-150
[2] Barret D, Lam T T, den Herder J W, et al. 2016 Proc. SPIE 9905 99052F
[3] Nagayoshi K, Ridder M L, Bruijn M P, Gottardi L, Taralli E, Khosropanah P, Akamatsu H, Visser S and Gao J R 2020 J. Low Temp. Phys. 199 943
[4] Bandler S R, Adams J S, Bailey C N, Busch S E, Chervenak J A, Eckart M E, Ewin A E, Finkbeiner F M, Kelley R L and Kelly D P 2013 IEEE Trans. Appl. Supercond. 23 2100705
[5] Weber J, Morgan K M, Yan D, Pappas C, Wessels A, O'Neil, Bennett D, Hilton G C, Swetz D S, Ullom J N and Schmidt D R 2020 Superconduct. Sci. Technol. 33 115002
[6] Wang H B, Lv Y, Li D X, Zhao Y, GAO B and Wang Z 2023 Chin. Phys. B 32 028501
[7] Zhang S, Xia J K, Sun T, Wu W T, Wu B J, Wang Y L, Cantor R, Han K, Zhou X P, Liu H R, Fan F Y, Guo S M, Liang J C, Li D H, Song Y R, Ju X D, Fu Q and Liu Z 2022 Nucl. Sci. Tech. 33 84
[8] Figueroa-Feliciano E, Cabrera B, Miller A J, Powell S F, Saab T and Walker A 2000 Nucl. Instrum. Methods Phys. Res. Sect. A 444 453
[9] Geng Y 2022 Characteristics of photon number resolving superconducting transition edge sensors Ph.D. dessertation (University of Sciences and Technology of China) (in Chinese)
[10] Li P Z, Geng Y, Zhang W, Zhong J Q, Wang Z, Zhou K M, Miao W, Ren Y, Wu F, Zhang K, Yao Q J and Shi S C 2022 J. Low Temp. Phys. 209 248
[11] Muramatsu H, Nahayoshi K, Hayashi T, Sakai K, Yamamoto R, Mitsuda K, Yamasaki N Y, Maehata K and Hara T 2016 J. Low Temp. Phys. 184 91
[12] Wu W T, Lin Z R, Ni Z, Li P Z, Liang T T, Zhang G F, Wang Y L, Ying L L, Peng W, Zhang W, Shi S C, You L X and Wang Z 2022 Chin. Phys. B 31 028504
[13] Hölzer G, Fritsch M, Deutsch M, Hartwig J and Förster E 1997 Phys. Rev. A 56 4554
[14] Lloyd S 1982 IEEE Trans. Inform. Theor. 28 129
[15] Levine, Z H, Gerrits T, Migdall A L, Samarov D V, Calkins B, Lita A E and Nam S W 2012 J. Opt. Soc. Am. B 29 2066
[16] Szymkowiak A E, Kelley R L, Moseley S H and Stahle C K 1993 J. Low Temp. Phys. 93 281
[17] Zhang W, Wang Z, Zhong J Q, Li P Z, Geng Y, Miao W, Ren Y, Zhou K M, Yao Q J and Shi S C 2021 IEEE Trans. Appl. Supercond. 31 2101205 |
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