首页> 外文会议>International conference on mathematics, computational methods reactor physics;MC 2009 >COMBINED USE OF NEUTRON AND GAMMA MULTIPLICITIES FOR DETERMINING SAMPLE PARAMETERS
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COMBINED USE OF NEUTRON AND GAMMA MULTIPLICITIES FOR DETERMINING SAMPLE PARAMETERS

机译:中子和γ多重性的组合使用以确定样品参数

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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:中子和伽马阶矩的表达式在文献中是已知的。对于中子,这些作为构建单,双和三重检测率分析表达式的基础,可用于从测得的多重率中展开样品参数。在这里,我们建议结合使用单个和联合中子和伽马多重性以及相应的检测率。算到三阶,有9个自阶乘和交叉阶乘矩,在此明确给出。对于伽马光子,还导出了对应的多重检测率的公式,与乘分矩相反,该检测率是所测量的量,并且还明确包含样本裂变率。 将伽玛计数添加到中子中会引入新的未知数,这些未知数与伽玛的生成,泄漏和检测有关。尽管未知数更多,但是可测量的力矩总数超过了未知数。另一方面,附加方程的结构比中子矩复杂得多,因此不可能进行解析反演。 因此,我们建议通过使用人工神经网络(ANN)来反转超定方程的非线性系统,该网络可以有效且准确地处理所测量量的非线性和冗余。 ANNs的使用在样品裂变率,泄漏倍增率和a比的中子多重率展开方面取得了良好的结果。使用伽玛多重速率的工作正在进行中,并将在会议上报告一些结果。

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