PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning
<|reference_start|>PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning: 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 (CVAE) yielding the best results. The study also highlights the impact of different detector types on classification accuracy, with CeBr$_{3}$ excelling in short measurement times and HPGe performing better in longer durations. The findings suggest the importance of selecting the appropriate detector and methodology based on specific application requirements.<|reference_end|>
@article{folz2024pgnaa,
    title={PGNAA Spectral Classification of Aluminium and Copper Alloys with
  Machine Learning},
    author={Henrik Folz, Joshua Henjes, Annika Heuer, Joscha Lahl, Philipp Olfert,
  Bjarne Seen, Sebastian Stabenau, Kai Krycki, Markus Lange-Hegermann, Helmand
  Shayan},
    journal={arXiv preprint arXiv:2404.14107},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.14107},
    primaryClass={cs.LG cond-mat.mtrl-sci}
}