Feynman variance-to-mean in the context of passive neutron coincidence counting

Croft, Stephen; Favalli, Andrea; Hauck, Danielle K; Henzlová, D.; Santi, Peter A.

doi:10.1016/j.nima.2012.05.042

Cited by 45 publications

(32 citation statements)

References 11 publications

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“…Using the deadtime-corrected S, D, T rates measured in the MSR and RTI approaches for each coincidence gate width setting, the experimental triggered-gate (f d , f t ) and random-gate (w 2 , w 3 ) GUF values can be extracted using Eqs. (3)- (6). Similarly, the theoretical triggered-gate and random-gate GUF values can be calculated based on Eqs.…”

Section: Impact Of Guf On Pu Assaymentioning

confidence: 99%

“…The latter approach is typically employed to perform Feynman variance-to-mean analysis, however, the information contained in the random-gate multiplicity distribution generated using the long delay may also be used to extract correlated events as shown in [5]. A link between the Feynman variance-to-mean analysis and PNMC was recently established and discussed in detail in [5][6][7] and relies on the realization that multiplicity distribution constructed from series of random-gates can itself be utilized to extract the genuine correlated multiplicity rates. This approach was termed Randomly Triggered Interrogation (RTI).…”

Section: Introductionmentioning

confidence: 99%

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The impact of gate width setting and gate utilization factors on plutonium assay in passive correlated neutron counting

Henzlová

Menlove

Croft

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

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a b s t r a c tIn the field of nuclear safeguards, passive neutron multiplicity counting (PNMC) is a method typically employed in non-destructive assay (NDA) of special nuclear material (SNM) for nonproliferation, verification and accountability purposes. PNMC is generally performed using a well-type thermal neutron counter and relies on the detection of correlated pairs or higher order multiplets of neutrons emitted by an assayed item. To assay SNM, a set of parameters for a given well-counter is required to link the measured multiplicity rates to the assayed item properties. Detection efficiency, die-away time, gate utilization factors (tightly connected to die-away time) as well as optimum gate width setting are among the key parameters. These parameters along with the underlying model assumptions directly affect the accuracy of the SNM assay. In this paper we examine the role of gate utilization factors and the single exponential die-away time assumption and their impact on the measurements for a range of plutonium materials. In addition, we examine the importance of item-optimized coincidence gate width setting as opposed to using a universal gate width value. Finally, the traditional PNMC based on multiplicity shift register electronics is extended to Feynman-type analysis and application of this approach to Pu mass assay is demonstrated.

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Section: Impact Of Guf On Pu Assaymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The impact of gate width setting and gate utilization factors on plutonium assay in passive correlated neutron counting

Henzlová

Menlove

Croft

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

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“…It was traditionally used for evaluation of subcritical reactivity in reactors during startup with a neutron source, and later in accelerator-driven systems [1]. Relatively recently this method received attention in the area of nuclear safeguards for purposes of detection of special nuclear materials and spent fuel assay [2][3][4]. One of the drawbacks of the traditional approach in safeguards applications was related to the use of thermal neutron detectors, which limited the performance in terms of time, type and activity of the radioactive or nuclear material and data completeness (information about the energy of particles is lost during slowing down process).…”

Section: Introductionmentioning

confidence: 99%

The neutron–gamma Feynman variance to mean approach: Gamma detection and total neutron–gamma detection (theory and practice)

Chernikova

Axell

Avdic

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

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Two versions of the neutron-gamma variance to mean (Feynman-alpha method or Feynman-Y function) formula for either gamma detection only or total neutron-gamma detection, respectively, are derived and compared in this paper. The new formulas have particular importance for detectors of either gamma photons or detectors sensitive to both neutron and gamma radiation. If applied to a plastic or liquid scintillation detector, the total neutron-gamma detection Feynman-Y expression corresponds to a situation where no discrimination is made between neutrons and gamma particles. The gamma variance to mean formulas are useful when a detector of only gamma radiation is used or when working with a combined neutron-gamma detector at high count rates. The theoretical derivation is based on the Chapman-Kolmogorov equation with the inclusion of general reactions and corresponding intensities for neutrons and gammas, but with the inclusion of prompt reactions only. A one energy group approximation is considered. The comparison of the two different theories is made by using reaction intensities obtained in MCNPX simulations with a simplified geometry for two scintillation detectors and a 252 Cf-source. In addition, the variance to mean ratios, neutron, gamma and total neutron-gamma, are evaluated experimentally for a weak 252 Cf neutron-gamma source, a 137 Cs random gamma source and a 22 Na correlated gamma source. Due to the focus being on the possibility of using neutron-gamma variance to mean theories for both reactor and safeguards applications, we limited the present study to the general analytical expressions for Feynman-alpha formulas.

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“…3. Applicability for all NMC methods: In addition to the wellstudied multiplicity method [10], in the recent years two new methods were introduced: the random trigger interval (RTI) [12] and the skewness-variance-mean (SVM) [13]. In the present study, although the bootstrap distribution is evaluated for the mass, the data manipulation is done directly on the detection signal, regardless of the method used to evaluate the mass.…”

Section: Introductionmentioning

confidence: 99%