Calculation methods of parameters in α particle measurement
BackgroundRadioactivity measurement is widely used in various fields of nuclear technology application. The measurement uncertainty, confidence interval and detection limit are important parameters in radioactive measurement. Different calculation methods may get different results, and the calculati...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Science Press
2024-11-01
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| Series: | He jishu |
| Subjects: | |
| Online Access: | http://www.hjs.sinap.ac.cn/zh/article/doi/10.11889/j.0253-3219.2024.hjs.47.110402/ |
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| Summary: | BackgroundRadioactivity measurement is widely used in various fields of nuclear technology application. The measurement uncertainty, confidence interval and detection limit are important parameters in radioactive measurement. Different calculation methods may get different results, and the calculation results directly affect some important and relevant decisions.PurposeThis study aims at the calculation method of parameters in α particle radioactivity measurement that is used properly.MethodsBoth the partial derivative method and Monte Carlo method were applied to determinate the important parameters of α particle radioactivity measurement in this study. Firstly, based on the measurement of α activity concentration in gas using Passivated Implanted Planar Silicon (PIPS) detector, the sources of uncertainty for the measurement results were analyzed in details. Then, measurement uncertainty, confidence limits, decision threshold and detection limit of α particle activity concentration under different input modes were derived and calculated by partial derivative and Monte Carlo methods, singly and jointly. Finally, calculation results were compared analyzed.ResultsThe results show that when the input uncertainty is higher than 10%, the relative deviation between confidence interval and uncertainty results obtained by the two calculation methods is greater than 15%. When the relative uncertainty of the input is small, the detection limit is about 2 times of the decision threshold.ConclusionsThe partial derivative method is widely used without consideration of the probability distribution of the input, hence not suitable for complex and special input models. Under this circumstances, Monte Carlo method can be used to obtain more reliable calculation results. The two approaches can be applied jointly in complementary ways. |
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| ISSN: | 0253-3219 |