Award Data

The ability to discriminate between threatening and benign sources depends on the sensitivity, accuracy, and identification speed of the detection equipment. High energy resolution of the radionuclide sensor is necessary to decrease the likelihood of false identification. However, few scintillators achieve better than 3% energy resolution at 662 keV, and none exceed 2.5%. The goal of this effort i ...

The goal of this program is to develop the mixed elpasolite scintillators in order to achieve an energy resolution of  (≤ 2.5% (approaching 2%) at 662 keV for crystal sizes of up to 2 inch by 2 inch.  In this project we will investigate compositional changes in selected mixed-elpasolite(s) in order to achieve very high energy resolution.  By incorporating 6Li, neutron detection will also be t ...

The goal of this program is low-cost production of large-volume radiation detector scintillators with enhanced-medium energy resolution of 2-3%, at costs approaching those of medium energy resolution detectors such as CsI. To achieve this goal, a two pronged approach will be pursued. First, we will employ materials engineering tools to improve the performance of an existing scintillator. Second, a ...

Scintillators are the major components of radiation detection systems used in identification of Special Nuclear Materials. One of their key properties is the energy resolution (ER) that links directly to the isotope identification capabilities. Currently, the materials that provide high energy resolution (such as LaBr3:Ce) are expensive, while the low cost materials (such as NaI:Tl) provide poor e ...

There is significant interest in multi-functional materials enabling gamma-ray spectroscopy, neutron/gamma pulse shape discrimination (PSD), ultra-fast response, and time-of-flight (TOF) neutron detection. These materials would be used in a variety of mission scenarios for the localization and monitoring of special nuclear materials. Commercial inorganic scintillators offer some of these character ...

Military operators must identify and catalogue the equipment they find when inspecting laboratory facilities. This information is used to determine the lab’s capabilities, including the lab’s potential for building weapons of mass destruction. Currently, operators use computer vision algorithms to help them classify equipment in pictures of laboratory environments. Unfortunately, current image ...

The Counter-UAS Reinforcement learning Environment (The CURE) is a flexible, architecture-agnostic reinforcement learning (RL) training, evaluation, and demonstration environment paired with a state-of-the-art benchmark agent. The CURE is designed to allow researchers and RL experts to design, train, and test groundbreaking deep RL agents to avoid or defeat counter-UAS (C-UAS) challenges while com ...

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The nuclear weapons community has traditionally focused on the next generation or level of weapon capability complimented by gathering and analysis of actual test data. Now, with the cessation of actual weapons testing, there is an increased need to use the actual test data in modeling techniques to better understand the phenomenology and potential effects of nuclear weapons. The first phase of ...