Reynold Suarez

Noble gas migration experiment to support the detection of underground nuclear explosions

A Noble Gas Migration Experiment injected 127Xe, 37Ar, and sulfur hexafluoride into a former underground nuclear explosion shot cavity. These tracer gases were allowed to migrate from the cavity to near-surface and surface sampling locations and were detected in soil gas samples collected using various on-site inspection sampling approaches. Based on this experiment we came to the following conclusions: (1) SF6 was enriched in all of the samples relative to both 37Ar and 127Xe. (2) There were no significant differences in the 127Xe to 37Ar ratio in the samples relative to the ratio injected into the cavity. (3) The migratory behavior of the chemical and radiotracers did not fit typical diffusion modeling scenarios.

Detection in subsurface air of radioxenon released from medical isotope production

In order to better understand potential backgrounds of Comprehensive-Nuclear Test-Ban Treaty on-site inspection relevant gases, a sampling campaign was performed near Canadian Nuclear Laboratories in the Ottawa River Valley, a major source of environmental radioxenon. First of their kind measurements of atmospheric radioxenon imprinted into the shallow subsurface from an atmospheric pressure driven force were made using current on-site inspection techniques. Both atmospheric and subsurface gas samples were measured and analyzed to determine radioxenon concentrations. These measurements indicate that under specific sampling conditions, on the order of ten percent of the atmospheric radioxenon concentration may be measured via subsurface sampling.

Real-Time Digital Signal-Processor Implementation of Self-Calibrating Pulse-Shape Discriminator for High-Purity Germanium

Pulse-shape analysis of the ionization signals from germanium gamma-ray spectrometers is a method for obtaining information that can characterize an event beyond just the total energy deposited in the crystal. However, as typically employed, this method is data-intensive requiring the digitization, transfer, and recording of electronic signals from the spectrometer. A hardware realization of a real-time digital signal processor for implementing a parametric pulse shape analysis is presented. Specifically, a previously developed method for distinguishing between single-site and multi-site gamma-ray interactions is demonstrated in an on-line digital signal processor, compared with the original off-line pulse-shape analysis routine, and shown to have no significant difference. Reduction of the amount of the recorded information per event is shown to translate into higher duty-cycle data-acquisition rates while retaining the benefits of additional event characterization from pulse-shape analysis.

Measurements of Argon-39 at the U20az underground nuclear explosion site

Pacific Northwest National Laboratory reports on the detection of 39Ar at the location of an underground nuclear explosion on the Nevada Nuclear Security Site. The presence of 39Ar was not anticipated at the outset of the experimental campaign but results from this work demonstrated that it is present, along with 37Ar and 85Kr in the subsurface at the site of an underground nuclear explosion. Our analysis showed that by using state-of-the-art technology optimized for radioargon measurements, it was difficult to distinguish 39Ar from the fission product 85Kr. Proportional counters are currently used for high-sensitivity measurement of 37Ar and 39Ar. Physical and chemical separation processes are used to separate argon from air or soil gas, yielding pure argon with contaminant gases reduced to the parts-per-million level or below. However, even with purification at these levels, the beta decay signature of 85Kr can be mistaken for that of 39Ar, and the presence of either isotope increases the measurement background level for the measurement of 37Ar. Measured values for the 39Ar measured at the site ranged from 36,000 milli- Becquerel/standard-cubic-meter-of-air (mBq/SCM) for shallow bore holes to 997,000 mBq/SCM from the rubble chimney from the underground nuclear explosion.

Neural Network Based State of Health Diagnostics for an Automated Radioxenon Sampler/Analyzer

Artificial neural networks (ANNs) are used to determine the state-of-health (SOH) of the Automated Radioxenon Analyzer/Sampler (ARSA). ARSA is a gas collection and analysis system used for non-proliferation monitoring in detecting radioxenon released during nuclear tests. SOH diagnostics are important for automated, unmanned sensing systems so that remote detection and identification of problems can be made without onsite staff. Both recurrent and feed-forward ANNs are presented. The recurrent ANN is trained to predict sensor values based on current valve states, which control air flow, so that with only valve states the normal SOH sensor values can be predicted. Deviation between modeled value and actual is an indication of a potential problem. The feed-forward ANN acts as a nonlinear version of principal components analysis (PCA) and is trained to replicate the normal SOH sensor values. Because of ARSAs complexity, this nonlinear PCA is better able to capture the relationships among the sensors than standard linear PCA and is applicable to both sensor validation and recognizing off-normal operating conditions. Both models provide valuable information to detect impending malfunctions before they occur to avoid unscheduled shutdown. Finally, the ability of ANN methods to predict the system state is presented.

Pressure Sensor Calibration using VIPA Hardware

The VIPA hardware uses a series of modules to control the system. One of the modules that the VIPA hardware uses is a 16-bit analog input module. The main purpose of this module is to read in a voltage. The inputs of these modules are connected directly to the voltage outputs of all the pressure sensors in the system. Because the sensors have different pressure and voltage output ranges, it is necessary to calibrate and scale the sensors so that the values make sense to the operator of the system.