Unfolding positron lifetime spectra with neural networks

A new method for unfolding mean lifetimes and amplitudes as well as lifetime distributions from positron lifetime spectra is suggested and partially tested in this paper. The method is based on the use of artificial neural networks ANNs.. By using data from simulated positron spectra, generated by a simulation program, an ANN can be trained to extract lifetimes and amplitudes as well as their distributions from a positron spectrum as an input. In principle, the method has the potential to unfold an unknown number of lifetimes and their distribution from a measured spectrum. So far, only a proof-of-principle type preliminary investigation was made by unfolding three or four discrete lifetimes. These investigations show that the task of designing a proper and efficient network is not trivial. To achieve unfolding a number of distributions requires both careful design of the network as well as long training times. In addition, the performance of the method in practical applications is depending on the quality of the simulation model. However, the chances of satisfying the above criteria appear to be good. When appropriately developed, a trained network could be a very effective and efficient alternative to the existing methods, with very short identification times. q1999 Elsevier Science B.V. All rights reserved