Unfolding positron lifetime spectra with neural networks

A new method for unfolding mean lifetimes and amplitudes as well as lifetim e distributions from positron lifetime spectra is suggested and partially t ested in this paper. The method is based on the use of artificial neural ne tworks (ANNs). By using data from simulated positron spectra, generated by a simulation program, an ANN can be trained to extract lifetimes and amplit udes as well as their distributions from a positron spectrum as an input. I n principle, the method has the potential to unfold an unknown number of li fetimes and their distribution from a measured spectrum. So far, only a pro of-of-principle type preliminary investigation was made by unfolding three or four discrete Lifetimes. These investigations show that the task of desi gning a proper and efficient network is not trivial. To achieve unfolding a number of distributions requires both careful design of the network as wel l as long training times. In addition, the performance of the method in pra ctical applications is depending on the quality of the simulation model. Ho wever, the chances of satisfying the above criteria appear to be good. When appropriately developed, a trained network could be a very effective and e fficient alternative to the existing methods, with very short identificatio n times. (C) 1999 Elsevier Science B.V. All rights reserved.