Event based neutron activation spectroscopy and analysis algorithm using MLE and metaheuristics

Abstract

Abstract. Techniques used in neutron activation analysis are often dependent on the experimental setup. In the context of developing a portable and high efficiency detection array, good energy resolution and half-life discrimination are difficult to obtain with traditional methods [1] given the logistic and financial constraints. An approach different from that of spectrum addition and standard spectroscopy analysis [2] was needed. The use of multiple detectors prompts the need for a flexible storage of acquisition data to enable sophisticated post processing of information. Analogously to what is done in heavy ion physics, gamma detection counts are stored as two-dimensional events. This enables post-selection of energies and time frames without the need to modify the experimental setup. This method of storage also permits the use of more complex analysis tools. Given the nature of the problem at hand, a light and efficient analysis code had to be devised. A thorough understanding of the physical and statistical processes [3] involved was used to create a statistical model. Maximum likelihood estimation was combined with metaheuristics to produce a sophisticated curve-fitting algorithm. Simulated and experimental data were fed into the analysis code prompting positive results in terms of half-life discrimination, peak identification and noise reduction. The code was also adapted to other fields of research such as heavy ion identification of the quasi-target (QT) and quasi-particle (QP). The approach used seems to be able to translate well into other fields of research.

Quasiprojectile and intermediate velocity isotopic ratios for light fragments emitted in theAr36+Ni58andGauthier1, Roy2, St-Onge3 et al. 2014Phys. Rev. C0000View full textAdd to dashboardCite

“…The source separation is achieved by a technique similar to the one described above, except that it makes use of the positions of the nucleons upon the beam axis instead of their parallel velocity. A complex statistical Gaussian fit [27] is performed to separate the QP and quasitarget sources from the MR and pre-equilibrium sources. The source overlap is then weak and they are well separated.…”