Artificial neural network as an effective tool to calculate parameters of positron annihilation lifetime spectra

TL;DR

This paper demonstrates that a multi-layer perceptron neural network can accurately and efficiently predict parameters of positron annihilation lifetime spectra, offering a promising alternative to traditional methods.

Contribution

It introduces a neural network approach for PALS spectrum analysis, providing a quick and accurate alternative to conventional techniques.

Findings

Neural network predictions closely match traditional methods.

The method offers a faster way to decompose PALS spectra.

Advantages include efficiency; disadvantages are discussed.

Abstract

The paper presents the application of the multi-layer perceptron regressor model for predicting the parameters of positron annihilation lifetime spectra using the example of alkanes in the solid phase. A good agreement of calculation results was found when comparing with the commonly used methods. The presented method can be used as an alternative quick and accurate tool for decomposition of PALS spectra in general. The advantages and disadvantages of the new method are discussed.