Analysis of positron lifetime spectra using Bayesian inference

Abstract

Positron annihilation is a well-established technique for producing spectra which can be analyzed for extracting physically meaningful parameters that characterize material defects and vacancies on an atomic scale. Mathematically, this is based on fitting a parameter-dependent model to the experimental data. Traditionally, this fit involves local nonlinear optimization routines that depend on a reasonable initial guess for the searched parameters. Therefore, very different sets of parameters may yield indistinguishably good fits for a given experimental spectrum but, give rise to ambiguities in data analysis. In order to alleviate them, a computer program has been developed for analyzing positron lifetime spectra by incorporating a global nonlinear optimization routine based on Simulated annealing (SA) into the Markov chain Monte-Carlo Bayesian inference algorithm (MCMC-BI) so that it provides a robust fitting tool and yields information on the reliability of the results. It is tested against experimental spectra, comparing the results with those from the well-established commercial programs.