This study introduces a novel algorithm to detect and identify radioactive materials in urban settings using time-series detector response data. To address the challenges posed by varying backgrounds and to enhance th...This study introduces a novel algorithm to detect and identify radioactive materials in urban settings using time-series detector response data. To address the challenges posed by varying backgrounds and to enhance the quality and reliability of the energy spectrum data, we devised a temporal energy window. This partitioned the time-series detector response data, resulting in energy spectra that emphasize the vital information pertaining to radioactive materials. We then extracted characteristic features of these energy spectra, relying on the formation mechanism and measurement principles of the gammaray instrument spectrum. These features encompassed aggregated counts, peak-to-flat ratios, and peak-to-peak ratios. This methodology not only simplified the interpretation of the energy spectra's physical significance but also eliminated the necessity for peak searching and individual peak analyses. Given the requirements of imbalanced multi-classification, we created a detection and identification model using a weighted k-nearest neighbors(KNN) framework. This model recognized that energy spectra of identical radioactive materials exhibit minimal inter-class similarity. Consequently, it considerably boosted the classification accuracy of minority classes, enhancing the classifier's overall efficacy. We also executed a series of comparative experiments. Established methods for radionuclide identification classification, such as standard KNN, support vector machine, Bayesian network, and random tree, were used for comparison purposes. Our proposed algorithm realized an F1 measure of 0.9868 on the time-series detector response data, reflecting a minimum enhancement of 0.3% in comparison with other techniques. The results conclusively show that our algorithm outperforms others when applied to time-series detector response data in urban contexts.展开更多
With a sample of 58 Fermi/GBM GRBs detected before 2009 May,we compare the spectral properties of GBM GRBs with those detected by CGRO/BTASE and HETE-2.Our results show that the spectral index distributions are very c...With a sample of 58 Fermi/GBM GRBs detected before 2009 May,we compare the spectral properties of GBM GRBs with those detected by CGRO/BTASE and HETE-2.Our results show that the spectral index distributions are very consistent with those observed by BATSE.However,the Ep distribution is quite different from that observed with BATSE and HETE-2.The GBM GRBs tend to be softer than the BATSE sample,but harder than the HETE-2 sample.This may be due to the instrumental selection effects and artificial sample effect on the BATSE sample.The distribution of the pseudo redshifts derived from the luminosity indicator based on the Amati-relation shows rough consistency with the spectroscopic redshifts of Swift GRBs.We estimate the detection rate of GBM GRBs with LAT based on the observed spectrum in the GBM band,and the inferred burst ratio of LAT detection with over 5 photons to GBM detection is 6%,yielding a detection rate pf 12 GRBs/yr with over 5 photons in the 1-300 GeV band.This is roughly consistent with the results in the first half year of Fermi operation.The low detection rate compared with theoretical predictions is a key for revealing the radiation mechanisms and particle acceleration of the prompt gamma-rays.展开更多
基金supported by the National Defense Fundamental Research Projects (Nos. JCKY2020404C004 and JCKY2022404C005)Sichuan Science and Technology Program (No. 22NSFSC0044)。
文摘This study introduces a novel algorithm to detect and identify radioactive materials in urban settings using time-series detector response data. To address the challenges posed by varying backgrounds and to enhance the quality and reliability of the energy spectrum data, we devised a temporal energy window. This partitioned the time-series detector response data, resulting in energy spectra that emphasize the vital information pertaining to radioactive materials. We then extracted characteristic features of these energy spectra, relying on the formation mechanism and measurement principles of the gammaray instrument spectrum. These features encompassed aggregated counts, peak-to-flat ratios, and peak-to-peak ratios. This methodology not only simplified the interpretation of the energy spectra's physical significance but also eliminated the necessity for peak searching and individual peak analyses. Given the requirements of imbalanced multi-classification, we created a detection and identification model using a weighted k-nearest neighbors(KNN) framework. This model recognized that energy spectra of identical radioactive materials exhibit minimal inter-class similarity. Consequently, it considerably boosted the classification accuracy of minority classes, enhancing the classifier's overall efficacy. We also executed a series of comparative experiments. Established methods for radionuclide identification classification, such as standard KNN, support vector machine, Bayesian network, and random tree, were used for comparison purposes. Our proposed algorithm realized an F1 measure of 0.9868 on the time-series detector response data, reflecting a minimum enhancement of 0.3% in comparison with other techniques. The results conclusively show that our algorithm outperforms others when applied to time-series detector response data in urban contexts.
基金support by the National Basic Research Program of China (Grant No. 2009CB824800)the National Natural Science Foundation of China (Grant No. 10873002)+1 种基金Guangxi SHI-BAI-QIAN Project (Grant No. 2007201)the Program for 100 Young and Middle-aged Disciplinary Leaders in Guangxi Higher Education Instituions,and the Research Foundation of Guangxi University (Grant No. M30520)
文摘With a sample of 58 Fermi/GBM GRBs detected before 2009 May,we compare the spectral properties of GBM GRBs with those detected by CGRO/BTASE and HETE-2.Our results show that the spectral index distributions are very consistent with those observed by BATSE.However,the Ep distribution is quite different from that observed with BATSE and HETE-2.The GBM GRBs tend to be softer than the BATSE sample,but harder than the HETE-2 sample.This may be due to the instrumental selection effects and artificial sample effect on the BATSE sample.The distribution of the pseudo redshifts derived from the luminosity indicator based on the Amati-relation shows rough consistency with the spectroscopic redshifts of Swift GRBs.We estimate the detection rate of GBM GRBs with LAT based on the observed spectrum in the GBM band,and the inferred burst ratio of LAT detection with over 5 photons to GBM detection is 6%,yielding a detection rate pf 12 GRBs/yr with over 5 photons in the 1-300 GeV band.This is roughly consistent with the results in the first half year of Fermi operation.The low detection rate compared with theoretical predictions is a key for revealing the radiation mechanisms and particle acceleration of the prompt gamma-rays.
