Neutron coincidence counting based on time interval analysis with one- and two-dimensional Rossi-alpha distributions: an application for passive neutron waste assay

Neutron coincidence counting is commonly used for the non-destructive assay of plutonium bearing waste or for safeguards verification measurements. A major drawback of conventional coincidence counting is related to the fact that a valid calibration is needed to convert a neutron coincidence count rate to a 240Pu equivalent mass (240Pueq). In waste assay, calibrations are made for representative waste matrices and source distributions. The actual waste however may have quite different matrices and source distributions compared to the calibration samples. This often results in a bias of the assay result. This paper presents a new neutron multiplicity sensitive coincidence counting technique including an auto-calibration of the neutron detection efficiency. The coincidence counting principle is based on the recording of one- and two-dimensional Rossi-alpha distributions triggered respectively by pulse pairs and by pulse triplets. Rossi-alpha distributions allow an easy discrimination between real and accidental coincidences and are aimed at being measured by a PC-based fast time interval analyser. The Rossi-alpha distributions can be easily expressed in terms of a limited number of factorial moments of the neutron multiplicity distributions. The presented technique allows an unbiased measurement of the 240Pueq mass. The presented theory—which will be indicated as Time Interval Analysis (TIA)—is complementary to Time Correlation Analysis (TCA) theories which were developed in the past, but is from the theoretical point of view much simpler and allows a straightforward calculation of deadtime corrections and error propagation. Analytical expressions are derived for the Rossi-alpha distributions as a function of the factorial moments of the efficiency dependent multiplicity distributions. The validity of the proposed theory is demonstrated and verified via Monte Carlo simulations of pulse trains and the subsequent analysis of the simulated data.