A supervised machine learning approach using naive Gaussian Bayes classification for shape-sensitive detector pulse discrimination in positron annihilation lifetime spectroscopy (PALS)

The acquisition of high-quality and non-artefact afflicted positron lifetime spectra is crucial for a profound analysis, i.e. the correct lifetime spectra decomposition for retrieving the true information. Since the introduction of digital positron lifetime spectrometers, this is generally realized by applying detector pulse discrimination with the help of software-based pulse filtering regarding area and/or shape of the detector pulses.