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Application of small sample BP neural network in quantitative analysis of EDXRF

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Journal of Physics: Conference Series 2019年第1期1423卷

作者： Fei Li Zhuoyao Tang Liangquan Ge Nanxing Wu Feng Cheng Kun Sun Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

Quantitative analysis of EDXRF is affected by matrix effect, randomness of nuclear radiation, interaction of elements, statistical fluctuation of radiation detection process, etc. Its algorithm needs to consider many factors, and the established functional relationship is often a complex non-linear function. Therefore, effective quantitative analysis method has always been the key research direction of spectral resolution technology. In this paper, we use MATLAB software to study the effect of BP neural network in quantitative analysis of EDXRF by establishing the non-linear relationship between the counting rate of each element and the content of a single element in a small sample. The experimental results show that the small sample neural network can establish a stable structure and be applied to quantitative analysis of EDXRF, but the number of samples restricts the accuracy of prediction results, most of which can only guarantee 20% to 30% relative error.

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Research on gamma spectrum semi-quantitative analysis based on convolutional neural network

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Journal of Physics: Conference Series 2019年第1期1423卷

作者： Fei Li Jing Wang Liangquan Ge Fangyan Hu Feng Cheng Kun Sun Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China

In the field of practical application of nuclear technology, one of the vital steps is the interpretation of gamma energy spectrum, which can obtain the type and content of radionuclides and achieve further analyzation. Convolutional neural network is one the most basic and effective algorithm structures in deep learning because it has local receptive field, weight sharing and down sampling structure, it solves the problem of parameter expansion caused by full connection and reduces the number of weights. That can be used to study the resolution of gamma-ray spectra of convolutional neural networks. This paper introduces the spectral forming principle of gamma spectra and the convolutional neural network, and USES the convolutional neural network to study the spectral decomposition of gamma spectra. In this paper, a multi-layer convolution neural network model is built based on C# software. The convolution neural network is applied to gamma-ray spectrum decomposition, and the U, Th, K nuclides are identified and semi-quantitatively calculated. By identifying and analysing different energy spectrum data, it is shown that the modified model structure can be applied to the spectral decomposition of gamma spectrum.

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First experimental constraints on WIMP couplings in the effective field theory framework from CDEX

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arXiv 2020年

作者： Wang, Y. Zeng, Z. Yue, Qian Yang, L.T. Kang, K.J. Li, Y.J. Agartioglu, M. An, H.P. Chang, J.P. Chen, J.H. Chen, Y.H. Cheng, J.P. Chiang, C.Y. Dai, W.H. Deng, Z. Geng, X.P. Gong, H. Guo, Q.J. Guo, X.Y. He, H.J. He, L. He, S.M. Hu, J.W. Huang, T.S. Huang, H.X. Jia, H.T. Jia, L.P. Li, H.B. Li, J.M. Li, J. Li, M.X. Li, R.M.J. Li, X. Li, Y.L. Liao, B. Lin, F.K. Lin, S.T. Liu, S.K. Liu, Y.D. Liu, Y.Y. Liu, Z.Z. Ma, H. Mao, Y.C. Nie, Q.Y. Ning, J.H. Pan, H. Qi, N.C. Qiao, C.K. Ren, J. Ruan, X.C. Shang, C.S. Sharma, V. She, Z. Singh, L. Singh, M.K. Sun, T.X. Tang, C.J. Tang, W.Y. Tian, Y. Wang, G.F. Wang, L. Wang, Q. Wang, Y.C. Wang, Y.X. Wang, Z. Wong, H.T. Wu, S.Y. Wu, Y.C. Xing, H.Y. Xu, Y. Xue, T. Yan, Y.L. Yi, N. Yu, C.X. Yu, H.J. Yue, J.F. Zeng, M. Zhang, B.T. Zhang, L. Zhang, F.S. Zhao, M.G. Zhou, J.F. Zhou, Z.Y. Zhu, J.J. Key Laboratory of Particle and Radiation Imaging Ministry of Education Department of Engineering Physics Tsinghua University Beijing100084 China Department of Physics Tsinghua University Beijing100084 China Institute of Physics Academia Sinica Taipei11529 Taiwan NUCTECH Company Beijing100084 China YaLong River Hydropower Development Company Chengdu610051 China College of Nuclear Science and Technology Beijing Normal University Beijing100875 China School of Physics Peking University Beijing100871 China Sino-French Institute of Nuclear and Technology Sun Yet-sen University Zhuhai519082 China Department of Nuclear Physics China Institute of Atomic Energy Beijing102413 China College of Physics Sichuan University Chengdu610065 China Department of Physics Banaras Hindu University Varanasi221005 India Department of Physics Beijing Normal University Beijing100875 China School of Physics Nankai University Tianjin300071 China

We present weakly interacting massive particles (WIMPs) search results performed using two approaches of effective field theory from the China Dark Matter Experiment (CDEX), based on the data from both CDEX-1B and CDEX-10 stages. In the nonrelativistic effective field theory approach, both time-integrated and annual modulation analyses were used to set new limits for the coupling of WIMP-nucleon effective operators at 90% confidence level (C.L.) and improve over the current bounds in the low mχ region. In the chiral effective field theory approach, data from CDEX-10 were used to set an upper limit on WIMP-pion coupling at 90% C.L. We for the first time extended the limit to the mχ 2 region. Copyright © 2020, The Authors. All rights reserved.

关键词： Galaxies

Flux Variations of Cosmic Ray Air Showers Detected by LHAASO-KM2A During a Thunderstorm on 10 June 2021

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arXiv 2022年

作者： Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Zhe Cao, Zhen Chang, J. Chang, J.F. Chen, E.S. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.J. Chen, Y. Cheng, H.L. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Long, W.J. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Nan, Y.C. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, B.D. Wang, C. Wang, H. Wang, H.G. Wang, J.C. Wang, J.S. Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Hebei Normal University Hebei Shijiazhuang050024 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Facultéde Sciences Universitéde Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Department of Engineering Physics Tsinghua University Beijing100084 China School

The Large High Altitude Air Shower Observatory (LHAASO) has three sub-arrays, KM2A, WCDA and WFCTA. The flux variations of cosmic ray air showers were studied by analyzing the KM2A data during the thunderstorm on 10 June 2021. The number of shower events that meet the trigger conditions increases significantly in atmospheric electric fields, with maximum fractional increase of 20%. The variations of trigger rates (increases or decreases) are found to be strongly dependent on the primary zenith angle. The flux of secondary particles increases significantly, following a similar trend with that of the shower events. To better understand the observed behavior, Monte Carlo simulations are performed with CORSIKA and G4KM2A (a code based on GEANT4). We find that the experimental data (in saturated negative fields) are in good agreement with simulations, assuming the presence of a uniform electric field of -700 V/cm with a thickness of 1500 m in the atmosphere above the observation level. Due to the acceleration/deceleration by the atmospheric electric field, the number of secondary particles with energy above the detector threshold is modified, resulting in the changes in shower detection rate. © 2022, CC BY-NC-SA.

关键词： Cosmic rays

Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

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arXiv 2021年

作者： Lalande, Alain Chen, Zhihao Pommier, Thibaut Decourselle, Thomas Qayyum, Abdul Salomon, Michel Ginhac, Dominique Skandarani, Youssef Boucher, Arnaud Brahim, Khawla de Bruijne, Marleen Camarasa, Robin Correia, Teresa M. Feng, Xue Girum, Kibrom B. Hennemuth, Anja Huellebrand, Markus Hussain, Raabid Ivantsits, Matthias Ma, Jun Meyer, Craig Sharma, Rishabh Shi, Jixi Tsekos, Nikolaos V. Varela, Marta Wang, Xiyue Yang, Sen Zhang, Hannu Zhang, Yichi Zhou, Yuncheng Zhuang, Xiahai Couturier, Raphael Meriaudeau, Fabrice ImViA laboratory University of Burgundy Dijon France MRI department University Hospital of Dijon Dijon France Femto-ST laboratory University of Franche-Comté Belfort France Cardiology department University Hospital of Dijon Dijon France CASIS Company Quetigny France National Engineering School of Sousse University of Sousse Sousse Tunisia LASEE laboratory National Engineering School of Monastir University of Monastir Monastir Tunisia Biomedical Imaging Group Rotterdam Erasmus MC Rotterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Centre of Marine Sciences University of Algarve Faro Portugal School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Biomedical Engineering University of Virginia Charlottesville United States Charité – Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Bremen Germany German Centre for Cardiovascular Research Berlin Germany Department of Mathematics Nanjing University of Science and Technology Nanjing China Data Analysis and Intelligent Systems Lab Department of Computer Science University of Houston Houston United States Medical Robotics and Imaging Lab Department of Computer Science University of Houston Houston United States National Heart and Lung Institute Imperial College London London United Kingdom College of Computer Science Sichuan University Chengdu China College of Biomedical Engineering Sichuan University Chengdu China School of Biological Science and Medical Engineering Beihang University Beijing China School of Data Science Fudan University Shanghai China

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge’s main objectives were twofold. First, to evaluate if deep learning methods can distinguish between normal and pathological cases. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures. © 2021, CC BY-NC-SA.

关键词： Cardiology

Research on Cloud Platform Service Based on Industrial Design Industrial Chains: Taking 'the research and develop demonstration platform of Sichuan Industrial Design Platform Service' as an example.

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IOP Conference Series: Materials Science and engineering 2019年第1期573卷

作者： Yujia Jia Renhua Zheng College of Nuclear and Automation Engineering Chengdu University of Technology Chengdu Sichuan 610059 China

In recent years, the government has vigorous carried out for the policy of industrial design industry, so that it has enabled the industry to develop rapidly. However, for the industrial design industry, the poor connection between the upstream and downstream industrial chains of industrial design has become an important factor which is restricting the development of this industry. This paper analysed why the industrial design industry chain cannot be linked closely through investigation and analysis as well as the pilot project in Sichuan. According to the pilot project of Sichuan Industrial Design Cloud Service Platform (SCID Platform), it realized the self-circulation mode of industrial design industry chain, and play a substantial role in promoting economic operation.

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Light yield and field dependence measurement in PandaX-II dual-phase xenon detector

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arXiv 2021年

作者： Huang, Zhou Abdukerim, Abdusalam Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Chen Cheng, Yunshan Cui, Xiangyi Fan, Yingjie Fang, Deqing Fu, Changbo Fu, Mengting Geng, Lisheng Giboni, Karl Gu, Linhui Guo, Xuyuan Han, Ke He, Changda He, Jinrong Huang, Di Huang, Yanlin Hou, Ruquan Ji, Xiangdong Ju, Yonglin Li, Chenxiang Li, Mingchuan Li, Shu Li, Shuaijie Lin, Qing Liu, Jianglai Lu, Xiaoying Luo, Lingyin Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Shaheed, Nasir Ning, Xuyang Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shang, Changsong Shen, Guofang Si, Lin Sun, Wenliang Tan, Andi Tao, Yi Wang, Anqing Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Zhou Wu, Mengmeng Wu, Weihao Xia, Jingkai Xiao, Mengjiao Xiao, Xiang Xie, Pengwei Yan, Binbin Yan, Xiyu Yang, Jijun Yang, Yong Yu, Chunxu Yuan, Jumin Yuan, Ying Zhang, Dan Zhang, Minzhen Zhang, Peng Zhang, Tao Zhao, Li Zheng, Qibin Zhou, Jifang Zhou, Ning Zhou, Xiaopeng Zhou, Yong School of Physics and Astronomy Shanghai Jiao Tong University MOE Key Laboratory for Particle Astrophysics and Cosmology Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China School of Physics Beihang University Beijing100191 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China School of Physics Peking University Beijing100871 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Shandong Qingdao266237 China Department of Physics University of Maryland College ParkMD20742 United States Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China School of Physics Sun Yat-Sen University Guangzhou510275 China School of Physics Nankai University Tianjin300071 China Institute of Modern Physics Fudan University Shanghai 200433 China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai 200093 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai 200240 China

The dual-phase xenon time projection chamber (TPC) is one of the most sensitive detector technology for dark matter direct search, where the energy deposition of incoming particle can be converted into photons and electrons through xenon excitation and ionization. The detector response to signal energy deposition varies significantly with the electric field in liquid xenon. We study the detector’s light yield and its dependence on the electric field in the PandaX-II dual-phase detector containing 580 kg liquid xenon in the sensitive volume. From our measurements, the light yield at electric fields from 0 V/cm to 317 V/cm is obtained for energy depositions up to 236 keV. Copyright © 2021, The Authors. All rights reserved.

关键词： Deposition

Dark Matter Search Results from the PandaX-4T Commissioning Run

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arXiv 2021年

作者： Meng, Yue Wang, Zhou Tao, Yi Abdukerim, Abdusalam Bo, Zihao Chen, Wei Chen, Xun Chen, Yunhua Cheng, Chen Cheng, Yunshan Cui, Xiangyi Fan, Yingjie Fang, Deqing Fu, Changbo Fu, Mengting Geng, Lisheng Giboni, Karl Gu, Linhui Guo, Xuyuan Han, Ke He, Changda He, Jinrong Huang, Di Huang, Yanlin Huang, Zhou Hou, Ruquan Ji, Xiangdong Ju, Yonglin Li, Chenxiang Li, Mingchuan Li, Shu Li, Shuaijie Lin, Qing Liu, Jianglai Lu, Xiaoying Luo, Lingyin Ma, Wenbo Ma, Yugang Mao, Yajun Shaheed, Nasir Ning, Xuyang Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shang, Changsong Shen, Guofang Si, Lin Sun, Wenliang Tan, Andi Wang, Anqing Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wu, Mengmeng Wu, Weihao Xia, Jingkai Xiao, Mengjiao Xiao, Xiang Xie, Pengwei Yan, Binbin Yan, Xiyu Yang, Jijun Yang, Yong Yu, Chunxu Yuan, Jumin Yuan, Ying Zhang, Dan Zhang, Minzhen Zhang, Peng Zhang, Tao Zhao, Li Zheng, Qibin Zhou, Jifang Zhou, Ning Zhou, Xiaopeng Zhou, Yong School of Physics and Astronomy Shanghai Jiao Tong University MOE Key Laboratory for Particle Astrophysics and Cosmology Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 200240 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China School of Physics Sun Yat-Sen University Guangzhou510275 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Physics and Particle Irradiation of Ministry of Education Shandong University Shandong Qingdao266237 China School of Physics Nankai University Tianjin300071 China Institute of Modern Physics Fudan University Shanghai 200433 China School of Physics Peking University Beijing100871 China School of Physics Beihang University Beijing100191 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai 200093 China Department of Physics University of Maryland College ParkMD20742 United States School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai 200240 China

We report the first dark matter search results using the commissioning data from PandaX-4T. Using a time projection chamber with 3.7-tonne of liquid xenon target and an exposure of 0.63 tonne·year, 1058 candidate events are identified within an approximate nuclear recoil energy window between 5 and 100 keV. No significant excess over background is observed. Our data set a stringent limit to the dark matter-nucleon spin-independent interactions, with a lowest excluded cross section (90% C.L.) of 3.8 × 10−47cm2 at a dark matter mass of 40 GeV/c2 Copyright © 2021, The Authors. All rights reserved.

关键词： Germanium alloys

Evaluation on least square method applied to gamma spectrum de-noising

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Journal of Physics: Conference Series 2019年第1期1423卷

作者： Fei Li Xinle Lang Yi Chen Liangquan Ge Li Feng Siwei Li Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province China College of Nuclear Technology and Automation Engineering Chengdu University of Technology China Chengdu Shude foreign language school

Gamma energy spectrum is a way to confirm the type and content of radioactive nuclides in substances. In order to obtain effective data of gamma spectrum, it is necessary to analyze the data of gamma spectrum obtained by the detector. Nevertheless, due to the influence of statistical fluctuation and gamma generated by the environment itself during the measurement of gamma spectrum, the obtained gamma spectrum is not accurate enough as a result of needing spectral analysis of gamma spectrum, and noise reduction can make the spectral data smoother. Therefore, this paper studies the noise reduction algorithm for gamma spectrum analysis, analyzes the design and application of different algorithms in the process of noise reduction, and summarizes the advantages and disadvantages of this algorithm and others.

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