Advanced fitting algorithms for analysing positron annihilation lifetime spectra

Abstract 2 The most common way to analyse PALS spectra involves fitting a parameter-dependent model to the experimental data. Traditionally, this fit involves local nonlinear optimisation routines that depend on a reasonable initial guess for the searched parameters. This, together with the fact that very different sets of parameters may yield indistinguishably good fits for a given experimental spectrum, gives rise to ambiguities in the data analysis in most but the simplest cases. In order to alleviate these difficulties, a computer program named PAScual was developed that incorporates two advanced algorithms to provide a robust fitting tool: on the one hand, it incorporates a global nonlinear optimisation routine based on the Simulated Annealing algorithm and, on the other hand, it yields information on the reliability of the results by means of a Markov Chain Monte-Carlo Bayesian Inference method. In this work the methods used in PAScual are described and tested against both simulated and experimental spectra, comparing the results with those from the well established program LTv9. The examples focus on the type of complex data that results from the study of self-assembled amphiphile materials containing co-existing aqueous and hydrocarbon regions