Fast adaptive alpha-particle spectrum fitting algorithm based on genetically estimated initial parameters

This work presents a high performance procedure for the unfolding of alpha particle spectra based on genetically estimated initial parameters. The process starts with the search for a globally optimized set of fitting parameters from a population of randomly generated solutions. The solution found with the genetic algorithm is then transferred to a Levenberg–Marquardt procedure in order to calculate the covariance matrix for the fit. The proposed method provides the set of final parameters and their associated standard deviation. Several fitted spectra demonstrate the effectiveness of the proposed method in searching for and finding initial conditions in a fast and automated process.