Suppressing Background Radiation Using Poisson Principal Component Analysis
This work addresses the problem of enhancing nuclear threat detection for security applications, but it appears incremental as it focuses on refining an existing method under specific conditions.
The paper investigates whether Poisson PCA, which uses a Poisson-based loss function, can outperform standard Gaussian PCA in modeling background radiation for nuclear threat detection systems, aiming to improve sensitivity and specificity.
Performance of nuclear threat detection systems based on gamma-ray spectrometry often strongly depends on the ability to identify the part of measured signal that can be attributed to background radiation. We have successfully applied a method based on Principal Component Analysis (PCA) to obtain a compact null-space model of background spectra using PCA projection residuals to derive a source detection score. We have shown the method's utility in a threat detection system using mobile spectrometers in urban scenes (Tandon et al 2012). While it is commonly assumed that measured photon counts follow a Poisson process, standard PCA makes a Gaussian assumption about the data distribution, which may be a poor approximation when photon counts are low. This paper studies whether and in what conditions PCA with a Poisson-based loss function (Poisson PCA) can outperform standard Gaussian PCA in modeling background radiation to enable more sensitive and specific nuclear threat detection.