Kernel-based Gaussian process for anomaly detection in sparse gamma-ray data.

ABSTRACT: In radioactive source surveying protocols, a number of task-inherent features degrade the quality of collected gamma ray spectra, including: limited dwell times, a fluctuating background, a large distance to the source, weak source activity, and the low sensitivity of mobile detectors. Thus, collected gamma ray spectra are expected to be sparse and noise dominated. For extremely sparse spectra, direct background subtraction is infeasible and many background estimation techniques do not apply. In this paper, we present a statistical algorithm for source estimation and anomaly detection under such conditions. We employ a fixed-hyperparameter Gaussian processes regression methodology with a linear innovation sequence scheme in order to quickly update an ongoing source distribution estimate wi

SUBMITTER: Romanchek GR 

PROVIDER: S-EPMC6977756 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature