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[en] A fast and robust methodology for identification of radioactive materials is of great interest for applications in the fields of homeland security and nuclear nonproliferation. For fissile materials in particular, several passive and active interrogation techniques are being investigated. These systems rely on the fact that the interrogated material emits correlated neutrons and gamma rays from each induced or spontaneous fission event. A convenient way to look at correlated events in a fissionable material is to study time-dependent detector cross-correlation functions. These functions are unique for a given material and geometry, representing a distinctive signature of the material-geometry configuration. In this work, we focus on the identification of plutonium samples in both metallic and oxide form. The simulation program used in this study is the MCNP-PoliMi code, an improved version of the standard MCNP code. This code allows the user to obtain event-by-event information about the simulated particles. This capability is essential for accurate simulations of cross-correlation events. The number of correlated events as a function of time is obtained from the MCNP-PoliMi output collision file by using appropriate post-processing algorithms. We investigated several parameters, such as the sample composition and mass, the sample-detector distance, and the shielding between the detector and the sample. We then analyzed the simulated cross correlation functions to obtain relationships between features from the correlations and sample characteristics. To verify the simulations, the measured correlation function was compared with the simulated one
[en] The authors have developed a novel method for analyzing active neutron multiplicity data. The conventional method was derived from the standard passive multiplicity equations known as the point model. The approach was to substitute a term consisting of the product of the interrogation source strength, the coupling coefficient, and the sample mass for the product term of the sample mass and the fission rate: Iκcm → F0m, where I is the source strength, κc is the coupling coefficient, m is the sample mass and F is the fission rate. Note that the sample mass, m, refers to the fissile material (e.g. 235U) in the active case and fertile material (e.g. 240Pueff) in the passive case. In addition, the spontaneous fission multiplicity coefficients, νs, were replaced with the induced fission multiplicity coefficients, νi. This model has several drawbacks. The most significant is that the coupling coefficient, κc, varies significantly with the multiplication. As a consequence, there is not a clear linear relationship between the doubles rate and the sample mass, nor is there a clear linear relationship between the multiplication-corrected doubles rate and the sample mass. This problem has limited the application of active neutron multiplicity counting. They propose here a novel approach to deriving the multiplicity equations. A different substitution is made in the point model equations. The value of alpha is replaced with a new term, alpha-prime: α → α(prime) (triplebond) α + κpI/Fm0νs1. There are several benefits to this approach, but most significant is that the new coupling coefficient, κp, remains constant. In this paper they will establish the general physics justification why this different substitution is appropriate. They will derive the new point model equations for active neutron multiplicity starting from the original point model equations and making the substitution above, and they will compare the two models.