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Presentation to be presented during the first annual Prompt Radiation Detection and Imaging Workshop, hosted by the NNSS Dynamic Instruments Team, April 25–28, 2022, at NLV C-01 Auditorium and via Webex.
Presentation to be presented during the first annual Prompt Radiation Detection and Imaging Workshop, hosted by the NNSS Dynamic Instruments Team, April 25–28, 2022, at NLV C-01 Auditorium and via Webex.
Presentation to be presented during the first annual Prompt Radiation Detection and Imaging Workshop, hosted by the NNSS Dynamic Instruments Team, April 25–28, 2022, at NLV C-01 Auditorium and via Webex.
The presentation at the first Prompt Radiation Detection and Imaging Workshop covered fast neutron and tomography work at the National Ignition Facility (NIF).
We describe a new concept for a basic radiation detection panel based on conventional scintillator technology and commercially available solid-state photo-detectors. The panels are simple in construction, robust, very efficient and cost-effective and are easily scalable in size, from tens of cm 2 to tens of m 2 . We describe two possible applications: flagging radioactive food coontamination and detection of illicit radio nucleides, such as those potentially used in a terrorist attack with a dirty bomb.
Near-term solutions are needed to allow for flexible engagement in future nuclear arms control discussions. This project developed a method for implementing an information barrier (IB) on commercial systems, shortening the research and development lifecycle for warhead verification technologies while offering improved and inherently flexible capabilities. The crux of the verification challenge remains the difficulty in developing an authenticatable IB which prevents sensitive host country information from inadvertent transmission to an inspector. Many concepts for IB’s rely on dedicated “trusted” processor modules developed with dedicated custom radiation detection systems and associated algorithms. Without a priori knowledge of the treaty item, the parameter space for measurements can be nearly infinite and robustness against spoofing without the ability to view sensitive data is key. This project has produced an unclassified framework capable of ingesting data from common gamma detectors and identifying the presence of weapons grade nuclear material at over 90% accuracy.
In unattended monitoring scenarios, automated radiation detection algorithms must be able to detect low signal-to-noise ratio (SNR) anomalies in a potentially dynamic and noisy background and report these anomalies in a timely fashion. Dynamic and noisy backgrounds complicate the use of simple gross-counting algorithms because they can lead to either high false positive rates or low sensitivity. Algorithms that use the entire spectrum have been the most successful in this area; notable examples are the NSCRAD algorithm developed at Pacific Northwest National Laboratory and recently the nonnegative matrix factorization approach developed at Lawrence Berkeley National Laboratory (LBNL). These approaches use either spectral regions of interest or spectral decomposition to detect threat isotopes in the background.
Introduction • Background: Silicon photomultipliers (SiPMs) are compact, low-power, and high-efficiency detectors that are increasingly used in radiation detection applications like medical imaging and nuclear safeguards. The have advantages of PMTs because they are smaller, operate at lower voltages, and offer high photon detection efficiency. • Challenges: SiPMs suffer from increased dark count rate (DCR) and optical crosstalk (OCT), which degrades performance. • Purpose of study: This study compares the Advansid ASD-NUV3S-P-40, Broadcom AFBR-S4K33C0147L, and Onsemi MicroFJ-30035-TSVTR under four different temperatures to compare their performance in harsh environments.
Light output response of scintillators is crucial to the utilization of organic scintillators as effective tools in radiation detection and measurement. While the response is a continuous distribution, the light output corresponding to the maximum energy deposition is crucial in effectively understanding and simulating a detector. There are a variety of fits derived in literature that will vary for every detector material. The Light Output Response Fitter, or LORF Program is a python script designed to easily and quickly compute and plot fits for a variety of scintillator light output models. It includes a stopping power library constructed from SRIM including Organic Glass, EJ309, EJ301, Stilbene, EJ276, and their deuterated counterparts by default, with the ability for the user to add custom stopping power libraries. The user is also capable of importing the python package and utilizing its in-built functions as appropriate. Uses for this capability include plotting and computing a model with known parameters.
This training manual is designed to provide end users with a comprehensive knowledge base for the safe and effective use of the Autonomous Radiation Cartographer (ARC) System. The ARC is a fully autonomous radiation detection robot based on the Spot Robot platform manufactured by Boston Dynamics.
Additive manufacturing techniques enable a wide range of possibilities for novel radiation detectors spanning simple to highly complex geometries, multi-material composites, and metamaterials that are either impossible or cost prohibitive to produce using conventional methods. The present work identifies a set of promising formulations of photocurable scintillator resins capable of neutron-gamma pulse shape discrimination (PSD) to support the additive manufacturing of fast neutron detectors. The development of these resins utilizes a step-by-step, trial-and-error approach to identify different monomer and cross-linker combinations that meet the requirements for 3D printing followed by a 2-level factorial parameter study to optimize the radiation detection performance, including light yield, PSD, optical clarity, and hardness. The formulations resulted in hard, clear, PSD-capable plastic scintillators that were cured solid within 10 s using 405 nm light. The best-performing scintillator produced a light yield 83% of EJ-276 and a PSD figure of merit equaling 1.28 at 450–550 keVee.
The advent of nanotechnology has motivated a revolution in the development of miniaturized sensors. Such sensors can be used for radiation detection, temperature sensing, radio-frequency sensing, strain sensing, and more. At the nanoscale, integrating the materials of interest into sensing platforms can be a common issue. One promising platform is photonic crystal fibers, which can draw in optically sensitive nanoparticles or have its optical properties changed by specialized nanomaterials. However, testing these sensors at scale is limited by the the need for specialized equipment to integrate these photonic crystal fibers into optical fiber systems. Having a method to enable rapid prototyping of new nanoparticle-based sensors in photonic crystal fibers would open up the field to a wider range of laboratories that could not have initially studied these materials in such a way before. This manuscript discusses the improved processes for cleaving, drawing, and rapidly integrating nanoparticle-based photonic crystal fibers into optical system setups. The method proposed in this manuscript achieved the following innovations: cleaving at a quality needed for nanoparticle integration could be done more reliably (≈100% acceptable cleaving yield versus ≈50% conventionally), nanoparticles could be drawn at scale through photonic crystal fibers in a safe manner (a method to draw multiple photonic crystal fibers at scale versus one fiber at a time), and the new photonic crystal fiber mount was able to be finely adjusted when increasing the optical coupling before inserting it into an optical system (before, expensive fusion splicing was the only other method).
Triaxial neutron stars can be sources of continuous gravitational radiation detectable by ground-based interferometers. The amplitude of the emitted gravitational wave can be greatly affected by the state of the hydrodynamical fluid flow inside the neutron star. In this work, we examine the most triaxial models along two sequences of constant rest mass, confirming their dynamical stability. We also study the response of a triaxial figure of quasiequilibrium under a variety of perturbations that lead to different fluid flows. Starting from the general relativistic compressible analog of the Newtonian Jacobi ellipsoid, we perform simulations of Dedekind-type flows. We find that in some cases the triaxial neutron star resembles a Riemann-S-type ellipsoid with minor rotation and gravitational wave emission as it evolves towards axisymmetry. The present results highlight the importance of understanding the fluid flow in the interior of a neutron star in terms of its gravitational wave content.
Two overarching requirements of crucial importance for the commercial and scientific establishment of semiconductor radiation detectors are: (1) exceptionally high-purity crystals with impurity concentrations less than 1 part-per-billion and (2) single crystals that are relatively free from subgrain boundaries, secondary phases, dislocations, and other electrically-active defects. We investigated the properties of thallium bromide (TlBr) material that affect its performance with the goal of increasing the material's commercial viability for radiation detection applications. For this purpose, we used beamlines at the National Synchrotron Light Source (NSLS) at Brookhaven National Laboratory (BNL), performing Micron-scale X-ray Detector Mapping, White Beam X-ray Diffraction Topography, and Micron-scale X-ray Fluorescence. These characterization methods improve the industry's understanding of TlBr and lead to the production of improved instrumentation. In this particular study we report electro-migration measurements of positive-ion Cu, Ag, and Au impurities in TlBr detectors under electric field strengths typically used for device operation. BNL improved TlBr detectors with an electrode design that corrects the response non-uniformities caused by crystal defects. This design can achieve improved energy resolution while using typical-grade, commercial crystals with relaxed quality requirements, thus reducing the overall cost of detectors. Additional characterization will be necessary to fully understand the structure and performance of TlBr with the ultimate goal to achieve the highest energy resolution. Our findings from past work will be presented along with our recommendations for additional investigation.
Pebble Bed Reactors are fueled with fuel pebbles that are circulated multiple times through the reactor vessel before discharge. During the normal operation of a PBR, ejected pebbles are returned to the reactor or discharged depending on the fuel burnup and physical condition of the pebbles. The burnup measurement is usually based on detected radiation signatures of fission products accumulated in the pebble fuel over burnup. Previous research has shown that height of photopeaks of fission products, such as 134Cs, 137Cs, 154Eu, etc., can be used independently or in combination to infer or predict the level of burnup in the fuel. However, it remains challenging to measure such complex sources due to self-shielding effects, strong radiation background and intervening materials. Another operational challenge is the required high throughput of burnup measurement, which necessitates limited measurement time and thus impacts quality of measured gamma-ray spectra. Hence, advanced spectral analysis methods are needed to analyze the noisy gamma spectra and predict the burnup values. We propose to use machine learning (ML) method to interpret gamma-ray spectra and predict the burnup values of the pebbles. ML has achieved widespread success and adoption across a few domains that require pattern recognition and analysis in varied data types. In this work, we apply three proven ML approaches - multilayer perceptrons, convolutional neural networks, and transformers - to the task of predicting fuel burnup from measured gamma spectra, and compile a dataset of simulated spectra for training and validation of the ML models. In this paper, we will discuss the network architecture of these three ML approaches and compare the performance of the simplest of these (MLP) to a standard linear regression.
Presentation to be presented during the first annual Prompt Radiation Detection and Imaging Workshop, hosted by the NNSS Dynamic Instruments Team, April 25–28, 2022, at NLV C-01 Auditorium and via closed Webex.
This presentation describes experimental measurements with thin scCVD diamond detectors and their DD and DT neutron sources. This presentation will be given at the NNSS Prompt Radiation Detection and Imaging Workshop April 25-28, 2022.
Presentation for the 1st Annual Prompt Radiation Detection and Imaging Workshop at NLV.