PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning

arXiv:2404.14107v1 Announce Type: new
Abstract: In this paper, we explore the optimization of metal recycling with a focus on real-time differentiation between alloys of copper and aluminium. Spectral data, obtained through Prompt Gamma Neutron Activation Analysis (PGNAA), is utilized for classification. The study compares data from two detectors, cerium bromide (CeBr$_{3}$) and high purity germanium (HPGe), considering their energy resolution and sensitivity. We test various data generation, preprocessing, and classification methods, with Maximum Likelihood Classifier (MLC) and Conditional Variational Autoencoder …

abstract analysis arxiv classification cond-mat.mtrl-sci copper cs.lg data detectors differentiation explore focus machine machine learning metal optimization paper prompt real-time recycling study through type