  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.
