Extracting energies and intensities from complex coincidence matrices

Coincidence intensities and uncertainties are extracted at the statistical limits from -y--y coincidence matrices. The matrices are decomposed into continuum, ridges and peaks. The continuum is successfully modeled by the product of two vectors which describe the Compton distributions in the coincident detectors or groups of detectors. The ridges are represented by the corresponding continuum vector scaled according to the intensity and energy of the associated -y-ray . The peaks are fitted as the product of two, one-dimensional Gaussians . This technique has been applied to the analysis of prompt gamma-rays from the spontaneous fission of 252Cf and the high spin states in t63Lu populated via the 122Sn(45 Sc, 4n) reaction . peak search . The most probable values and the uncertainties of energies and intensities are extracted from a two-dimensional fit . The process has been tested with coincidence data acquired in two different experiments . The heavy-ion reaction 122Sn(45 Sc, 4n)163Lu [3] data was acquired with an array of eight unsuppressed Ge detectors gated by high gamma-ray multiplicity . The data from the spontaneous fission of 252Cf [4] consisting of prompt coincidences were obtained with seven suppressed Ge detectors and a small planar detector (LEPS). Our first approach to direct decomposition of a coincidence matrix resulted in a successful simplification of the continuum function [5] . In subsequent developments we assumed the ridge associated with each peak found in the projection required its own description and that they-y coincidences were defined as the intersections at one of those peaks in each axis . This brought us back to the problems faced by the traditional method, due to the poor correlation between peaks in the projection and individual gamma rays . However, we noticed that the shape of the ridges for the most intense gamma-rays had the same profile as the corresponding continuum vector . This led to a universal model for all the ridges parallel to a given axis . Once the continuum and ridges were accounted for, a two-dimensional peak search gave us the location of the coincidence peaks and we were able to take full advantage of the two-dimensional nature of the data . C N L U s= 0 U 1000000