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COMBINED USE OF NEUTRON AND GAMMA MULTIPLICITIES FOR DETERMINING SAMPLE PARAMETERS

机译:结合中子和伽马多样性来确定样品参数

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Expressions for neutron and gamma factorial moments are known in the literature. For neutrons, these served as the basis of constructing analytic expressions for the detection rates of singles, doubles and triples, which can be used to unfold sample parameters from the measured multiplicity rates. Here we suggest the combined use of both the individual and joint neutron and gamma multiplicities and the corresponding detection rates. Counting up to third order, there are nine auto- and cross factorial moments, which are all given here explicitly. For the gamma photons, formulae are derived also for the corresponding multiplicity detection rates which, in contrast to the factorial moments, are the measured quantities and which also contain the sample fission rate explicitly. Adding the gamma counting to the neutrons introduces new unknowns, related to gamma generation, leakage, and detection. Despite more unknowns, the total number of measurable moments exceeds the number of unknowns. On the other hand, the structure of the additional equations is substantially more complicated than the neutron moments, hence their analytical inversion is not possible. We suggest therefore to invert the non-linear system of over-determined equations by using artificial neural networks (ANN), which can handle both the non-linearity and the redundance in the measured quantities in an effective and accurate way. The use of ANNs is demonstrated with good results on the unfolding of neutron multiplicity rates for the sample fission rate, the leakage multiplication and the a ratio. Work with using the gamma multiplicity rates is on-going and some results will be reported at the conference.
机译:中子和伽马造成矩的表达在文献中是已知的。对于中子,这些是为构建单打,双打和三元组检测速率构建分析表达的基础,该分析表达式可以用于从测量的多个速率下展开样品参数。在这里,我们建议使用个人和联合中子和伽马多样性和相应的检测率。计算最多,有九个自动和跨因素的瞬间,这些时刻在这里明确给出。对于伽马光子,用于相应的多个检测速率,该配方也是与因子矩相比的相应的多样性检测速率,其是测量的量并且还明确地包含样品裂变率。将伽玛计数添加到中子介绍了与伽玛生成,泄漏和检测相关的新未知数。尽管有更多未知数,可衡量的时刻总数超过未知数。另一方面,附加方程的结构比中子矩显着复杂,因此它们的分析反演是不可能的。因此,我们建议通过使用人工神经网络(ANN)来反转过固定方程的非线性系统,其可以以有效且准确的方式处理非线性和在测量的数量中的冗余。在采样裂变率的中子多重速率的展开展开的情况下,对ANNS的使用具有良好的结果,泄漏倍增和A比率。使用使用伽玛多重率的工作正在进行中,会议将在会议上报告一些结果。

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