Non-negative Matrix Factorization of Gamma-Ray Spectra for Background Modeling, Detection, and Source Identification
Published in IEEE Transactions on Nuclear Science, 2019
Recommended citation: K. J. Bilton et al., "Non-negative Matrix Factorization of Gamma-Ray Spectra for Background Modeling, Detection, and Source Identification," in IEEE Transactions on Nuclear Science, vol. 66, no. 5, pp. 827-837, May 2019. doi: 10.1109/TNS.2019.2907267 https://ieeexplore.ieee.org/document/8673769
This paper introduces a method for modeling gamma-ray spectra using non-negative matrix factorization (NMF). Specifically, intuitive modes of variation in background radiation are learned and used for anomaly detection (source detection) and classification (source identification). The methods introduced here show improvements over traditional approaches, including a PCA-based approach to detection.
A preprint of this paper can be found here.