The GERDA (GERmanium Detector Array) is an experiment for the search of neutrinoless double beta decay (0 nu beta beta) in Ge-76, located at Laboratori Nazionali del Gran Sasso of INFN (Italy). In the first phase of the experiment, a 90% confidence level (C.L.) sensitivity of 2.4 . 10(25) yr on the 0 nu beta beta decay half-life was achieved with a 21.6 kg.yr exposure and an unprecedented background index in the region of interest of 10(-2) counts/(keV.kg.yr). No excess of signal events was found, and an experimental lower limit on the half-life of 2.1 . 10(25) yr (90% C.L.) was established. Correspondingly, the limit on the effective Majorana neutrino mass is m(ee) < 0.2-0.4 eV, depending on the considered nuclear matrix element. The previous claim for evidence of a 0 nu beta beta decay signal is strongly disfavored, and the field of research is open again

Networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spectroscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments. This study investigates the use of LiDAR and streaming video to enable real-time object detection and tracking, and the fusion of this tracking information with radiological data for the purposes of enhanced situational awareness and increased detection sensitivity. This work presents an object detection, tracking, and novel source-object attribution analysis that is capable of operating in real-time. By implementing this analysis pipeline on a custom developed system that comprises a static 2 × 4 × 16 inch NaI(Tl) detector co-located with a 64-beam LiDAR and 4 monocular cameras, we demonstrate the ability to accurately correlate trajectories from tracked objects to spectroscopic gamma-ray data in real time, and use physics-based models to reliably discriminate between source-carrying and non-source-carrying objects. In this work, we describe our approach in detail and present a quantitative performance assessment that characterizes the source-object attribution capabilities of both video and Li-DAR. Additionally, we demonstrate the ability to simultaneously track pedestrians and vehicles in a mock urban environment, and use this tracking information to improve both detection sensitivity and situational awareness using our contextual-radiological data fusion methodology.

Static gamma-ray detector systems that are deployed outdoors for radiological monitoring purposes experience time- and spatially-varying natural backgrounds and encounters with man-made nuisance sources. In order to be sensitive to illicit sources, such systems must be able to distinguish those sources from benign variations due to, e.g., weather and human activity. In addition to fluctuations due to non-threats, each detector has its own response and energy resolution, so providing a large network of detectors with predetermined background and source templates can be an onerous task. Instead, we propose that static detectors use simple physics-informed algorithms to automatically learn the background and nuisance source signatures, which can them be used to bootstrap and feed into more complex algorithms. Specifically, we show that non-negative matrix factorization (NMF) can be used to distinguish static background from the effects of increased concentrations of radon progeny due to rainfall. We also show that a simple process of using multiple gross count rate filters can be used in real time to classify or “triage” spectra according to whether they belong to static, rain, or anomalous categories for processing with other algorithms. If a rain sensor is available, we propose a method to incorporate that signal as well. Two clustering methods for anomalous spectra are proposed, one using Kullback-Leibler divergence and the other using regularized NMF, with the goal of finding clusters of similar spectral anomalies that can be used to build anomaly templates. Finally we describe the issues involved in the implementation of some of these algorithms on deployed sensor nodes, including the need to monitor the background models for long-term drifting due to physical changes in the environment or changes in detector performance.

Integration of contextual sensors into vehicle-borne mobile radiation detection systems delivers a rich description of the environment to inform estimates of the complex and variable gamma-ray signals observed in urban areas. Models based on these data streams could provide realistic inputs to urban radiological search algorithms and potentially improve the system's sensitivity to detect illicit radiological and nuclear materials. In this work, LiDAR and inertial data are combined using simultaneous localization and mapping techniques to create a three-dimensional (3D) representation of the surrounding scenery. Semantic segmentation of concurrently collected video imagery enables the division of the 3D model into distinct material categories. The radioactive flux of surfaces associated with these categories are inferred through maximum likelihood estimation maximization and the activity of the three most common isotopes (K-40, U-238 series, and Th-232 series) in the respective materials is predicted. The results, found to be in agreement with ground truth measurements performed at the facility, suggest that it is possible to quickly infer the composition of naturally occurring materials in structures that comprise a radiological scene. Such a capability could be used to inform radiological search algorithms and enable data-driven modeling of radiological search problems, which could facilitate system testing and operator training activities.

When searching for radiological sources in an urban area, a vehicle-borne detector system will often measure complex, varying backgrounds primarily from natural gamma-ray sources. Much work has been focused on developing spectral algorithms that retain sensitivity and minimize the false-positive rate even in the presence of such spectral and temporal variability. However, information about the environment surrounding the detector system might also provide useful clues about the expected background, which if incorporated into an algorithm, could improve performance. Recent work has focused on extensive measuring and modeling of urban areas with the goal of understanding how these complex backgrounds arise. This work presents an analysis of panoramic video images and gamma-ray background data collected in Oakland, California, by the radiological multisensor analysis platform (RadMAP) vehicle. Features were extracted from the panoramic images by semantically labeling the images and then convolving the labeled regions with the detector response. A linear model was used to relate the image-derived features to gamma-ray spectral features obtained using nonnegative matrix factorization (NMF) under different regularizations. We find some gamma-ray background features correlate strongly with image-derived features that measure the response-adjusted solid angle subtended by sky and buildings, and we discuss the implications for the development of future, contextually aware detection algorithms.

Recent results from RIKEN/RIBF on the low-lying level structure of 29F are interpreted within the Particle-Rotor Model. We show that the experimental data can be understood in the Rotation-aligned Coupling Scheme, with the 5/2+ ground state as the bandhead of a decoupled band. In this picture, the energy of the observed 1/2+1 state correlates strongly with the rotational energy of the core and provides an estimate of the 2+ energy in 28O. Our analysis suggest a moderate deformation, epsilon_2 ~ 0.17, and places the 2+ in 28O at ~ 2.4 MeV.Comment: 3 pages, 3 figures, To be submitted to Phys. Lett.