Browsing by Author "Aaltonen, Topi"

Positron annihilation lifetime spectroscopy (PALS) is a method used to analyse the properties of materials, namely their composition and what kind of defects they might consist of. PALS is based on the annihilation of positrons with the electrons of a studied material. The average lifetime of a positron coming into contact with a studied material depends on the density of electrons in the surroundings of the positron, with higher densities of electrons naturally resulting in faster annihilations on average. Introducing positrons in a material and recording the annihilation times results in a spectrum that is, in general, a noisy sum of exponential decays. These decay components have lifetimes that depend on the different density areas present in the material, and relative intensities that depend on the fractions of each area in the material. Thus, the problem in PALS is inverting the spectrum to get the lifetimes and intensities, a problem known as exponential analysis in general. A convolutional neural network architecture was trained and tested on simulated PALS spectra. The aim was to test whether simulated data could be used to train a network that could predict the components of PALS spectra accurately enough to be usable on spectra gathered from real experiments. Reasons for testing the approach included trying to make the analysis of PALS spectra more automated and decreasing user-induced bias compared to some other approaches. Additionally, the approach was designed to require few computational resources, ideally being trainable and usable on a single computer. Overall, testing showed that the approach has some potential, but the prediction performance of the network depends on the parameters of the components of the target spectra, with likely issues being similar to those reported in previous literature. In turn, the approach was shown to be sufficiently automatable, particularly once training has been performed. Further, while some bias is introduced in specifying the variation of the training data used, this bias is not substantial. Finally, the network can be trained without considerable computational requirements within a sensible time frame.