Using Replicative Assessment Of Spectroscopic Equipment (rase) Software To Develop Virtual Radiation Detection Equipment Testing Capabilities

A recent effort to validate the performance of the Replicative Assessment of Spectroscopic Equipment (RASE) software has been undertaken at Pacific Northwest National Laboratory by the Science and Engineering Team (SET) for the Department of Energy National Nuclear Security Administration’s Office of Nuclear Smuggling Detection and Deterrence (NSDD). NSDD supports a large array of testing and evaluation campaigns to assess the performance of radiation detection instruments in various mission spaces prior to their deployment. These campaigns are necessary to determine whether a system will fulfill the desired detection and performance capabilities of that mission space, yet they require large investments for the procurement of Systems Under Test, as well as scientist’s time for in-laboratory testing and data analysis. RASE was developed to enable virtual testing of radiation detection instruments in a simulated environment. RASE down-samples and adjusts “base spectra” created from physically obtained source measurements to generate simulated spectra for a variety of source isotopes, background environments, dose/flux rates, and measurement durations. When combined with a manufacturer-provided “replay tool”, these spectra can be processed using the instrument algorithm to obtain simulated real-world responses. The adoption of RASE into future test campaigns offers resiliency by using simulated testing capabilities rather than relying solely on in-field measurements, best serving the ongoing transition toward a more virtual and remote landscape. RASE allows test and evaluation teams to select only those instruments that pass predefined detection and/or identification thresholds while informing what in-field measurements would need to be conducted for the desired evaluation criteria. New algorithms and updates can also be tested in RASE prior to deployment. While RASE has the potential to reduce future testing costs, a broad validation effort had not yet been performed. This validation effort will be summarized, with results compared to experimental data from several previous in-field test campaigns.