arXiv subjects

Jayson R. Vavrek

Publications and source records attributed to Jayson R. Vavrek.

12 recordsLinked to original sources

arXiv2026

Radiological mapping and uncertainty quantification by a fast Microcanonical Langevin Monte Carlo sampler

Radiological mapping plays a critical role in nuclear emergency response and environmental management activities. A radiation image, representing the spatial and intensity distribution of the radioactivity, is reconstructed from the radiation data and the associated contextual information. Typical image reconstruction methods, such as Maximum Likelihood Expectation-Maximization (ML-EM), only provide point estimates of the pixel or voxel activities without associated uncertainties. Here, we apply a new Microcanonical Langevin Monte Carlo (MCLMC) sampler for radiation image reconstruction and uncertainty quantification. The MCLMC sampler properties are first tested with synthetic radiation images. Methods to obtain the radiation distribution estimate and the associated uncertainty from the samples drawn by MCLMC are discussed. Given sufficient measurement statistics, the radiation distribution estimate obtained from MCLMC results closely resembles the ground truth with less risk of over- or under-fitting compared to ML-EM. When MCLMC is run in parallel on a GPU, the samples can converge to the posterior distribution in about 10 seconds for an image with $10^3$--$10^4$ pixels, which is significantly faster than other comparable Markov Chain Monte Carlo (MCMC) samplers. We also tested MCLMC on a dataset from a real distributed source radiological mapping campaign, and the reconstructed results agree well with the expected activity map. The fast MCLMC sampler therefore enables improved imaging accuracy and prompt uncertainty quantification for reconstructed radiation images, which can better inform decision-making in response to radiological events.

physics.med-ph↗

arXiv2026

Machine learning methods for spectroscopic information recovery under ultrafast photon pileup

We present methods for recovering spectroscopic information from multiple concurrent photon interactions that would normally be lost due to pulse pileup. In particular, we focus on machine learning methods to recover information based on spatial (rather than temporal) energy deposition patterns in position-sensitive detectors. We construct two representative problems, namely (1) recovering the fraction of total energy deposition stemming from a monoenergetic signal vs. a smooth background; and (2) recovering the signal multiplicity, i.e., the number of interacting photons, in a pure-source-term example. In the signal fraction recovery problem, we use 3D convolutional neural networks (CNNs), fully-connected neural networks (FCNNs), a network based on the PointNet++ architecture, and two non-machine-learning methods to estimate the signal fraction in synthetic data when up to 20 total piled-up photons are present. The CNN, FCNN, and PointNet++ models reconstruct the signal energy deposition fractions with root mean square errors (RMSEs) of $14.5\%$, $18.8\%$, and $16.8\%$ given training datasets that fit in-core, while the classical methods perform poorly and will not improve with additional training data. In the multiplicity recovery problem, we demonstrate that, when trained with synthetically-piled-up real Cs-137 data, the 3D CNN architecture can recover the multiplicity with sub-photon RMSE, outperforming non-ML baselines. These methods can be adapted to future, more specific photon active interrogation applications, helping to re-enable spectroscopic analyses in those domains.

physics.ins-det↗

arXiv2026

Surrogate distributed radiological sources III: quantitative distributed source reconstructions

In this third part of a multi-paper series, we present quantitative image reconstruction results from aerial measurements of eight different surrogate distributed gamma-ray sources on flat terrain. We show that our quantitative imaging methods can accurately reconstruct the expected shapes, and, after appropriate calibration, the absolute activity of the distributed sources. We conduct several studies of imaging performance versus various measurement and reconstruction parameters, including detector altitude and raster pass spacing, data and modeling fidelity, and regularization type and strength. The imaging quality performance is quantified using various quantitative image quality metrics. Our results confirm the utility of point source arrays as surrogates for truly distributed radiological sources, and advance the quantitative capabilities of Scene Data Fusion gamma-ray imaging methods.

physics.ins-det↗

arXiv2026

Transferability of data-driven optimization results across multiple pixelated CdZnTe spectrometers

Recent work by Vavrek et al. (2025) showed that machine learning methods can be used to exploit spatial patterns of performance variations within the highly-segmented H3D M400 gamma spectrometer to improve an overall spectroscopic performance metric. That work also introduced the spectre-ml software, which tests various greedy, heuristic, random, and machine learning clustering algorithms to find the best performing mask for excluding detector regions to improve a user-defined performance metric by training on a given dataset. In this work, we build off of Vavrek et al. (2025) and seek to determine to what extent an optimized binary voxel mask trained on a given dataset can generalize to other datasets. In particular, this paper evaluates the transferability of masks trained on one M400 dataset to another M400 detector, in order to determine whether the total effort required in designing masks for different detectors and applications can be substantially reduced by using a single common mask. It also examines testing and training on different subsets of the same dataset to determine the natural level of variability in optimization results. In the inter-detector analysis, as expected, the best performing model on each detector is often one trained on that dataset, with an average performance enhancement of $16\%$ when considering the relative uncertainty in a Doniach fit to the $186$ keV peak. In comparison, the best transferred masks, with the best on average performance metric across all six detectors, show only a slightly smaller improvement of $13\%$ on average. These results suggest that high-performing, well-transferable masks can be shared among detectors, reducing or even eliminating the laborious processes of collecting a training dataset and performing the optimization for each detector, ultimately improving safeguards efficiency.

physics.ins-det↗

arXiv2026

Inter-detector differential fuzz testing for tamper detection in gamma spectrometers

We extend physical differential fuzz testing as an anti-tamper method for radiation detectors [Vavrek et al., Science and Global Security 2025] to comparisons across multiple detector units. The method was previously introduced as a tamper detection method for authenticating a single radiation detector in nuclear safeguards and treaty verification scenarios, and works by randomly sampling detector configuration parameters to produce a sequence of spectra that form a baseline signature of an untampered system. At a later date, after potential tampering, the same random sequence of parameters is used to generate another series of spectra that can be compared against the baseline. Anomalies in the series of comparisons indicate changes in detector behavior, which may be due to tampering. One limitation of this original method is that once the detector has `gone downrange' and may have been tampered with, the original baseline is fixed, and a new trusted baseline can never be established if tests at new parameters are required. In this work, we extend our anti-tamper fuzz testing concept to multiple detector units, such that the downrange detector can be compared against a trusted or `golden copy' detector, even despite normal inter-detector manufacturing variations. We show using three NaI detectors that this inter-detector differential fuzz testing can detect a representative attack, even when the tested and golden copy detectors are from different manufacturers and have different performances. Here, detecting tampering requires visualizing the comparison metric vs. the parameter values and not just the sample number; moreover this baseline is non-linear and may require anomaly detection methods more complex than a simple threshold. Overall, this extension to multiple detectors improves prospects for operationalizing the technique in real-world treaty verification and safeguards contexts.

physics.ins-det↗

arXiv2025

Data-driven optimization of pixelated CdZnTe spectrometers for uranium enrichment assay

In recent work [Vavrek et al. (2025)], we developed the performance optimization framework spectre-ml for gamma spectrometers with variable performance across many readout channels. The framework uses non-negative matrix factorization (NMF) and clustering to learn groups of similarly-performing channels and sweep through various learned channel combinations to optimize the performance tradeoff of including worse-performing channels for better total efficiency. In this work, we integrate the pyGEM uranium enrichment assay code with our spectre-ml framework, and show that the U-235 enrichment relative uncertainty can be directly used as an optimization target. We find that this optimization reduces relative uncertainties after a 30-minute measurement by an average of 20%, as tested on six different H3D M400 CdZnTe spectrometers, which can significantly improve uranium non-destructive assay measurement times in nuclear safeguards contexts. Additionally, this work demonstrates that the spectre-ml optimization framework can accommodate arbitrary end-user spectroscopic analysis code and performance metrics, enabling future optimizations for complex Pu spectra.

physics.ins-det↗

arXiv2025

Data-driven performance optimization of gamma spectrometers with many channels

In gamma spectrometers with variable spectroscopic performance across many channels (e.g., many pixels or voxels), a tradeoff exists between including data from successively worse-performing readout channels and increasing efficiency. Brute-force calculation of the optimal set of included channels is exponentially infeasible as the number of channels grows, and approximate methods are required. In this work, we present a data-driven framework for attempting to find near-optimal sets of included detector channels. The framework leverages non-negative matrix factorization (NMF) to learn the behavior of gamma spectra across the detector, and clusters similarly-performing detector channels together. Performance comparisons are then made between spectra with channel clusters removed, which is more feasible than brute force. The framework is general and can be applied to arbitrary, user-defined performance metrics depending on the application. We apply this framework to optimizing gamma spectra measured by H3D M400 CdZnTe spectrometers, which exhibit variable performance across their crystal volumes. In particular, we show several examples optimizing various performance metrics for uranium and plutonium gamma spectra in nondestructive assay for nuclear safeguards, and explore trends in performance vs.\ parameters such as clustering algorithm type. We also compare the NMF+clustering pipeline to several non-machine-learning algorithms, including several greedy algorithms. Overall, we find that the NMF+clustering pipeline tends to find the best-performing set of detector voxels, significantly improving over the un-optimized spectra, but that a greedy accumulation of spectra segmented by detector depth can in some cases give similar performance improvements in much less computation time.

physics.ins-det↗

arXiv2025

Demonstration of a new CLLBC-based gamma- and neutron-sensitive free-moving omnidirectional imaging detector

We have developed a CLLBC-based gamma- and neutron-sensitive multi-channel omnidirectional imaging detector, suitable for handheld or vehicle-borne operation and capable of quantitative radiation mapping in 3D. The system comprises 62 CLLBC modules in an active-masked configuration, and is coupled to a Localization and Mapping Platform (LAMP) suite of contextual sensors that provides a 3D map of the environment. The contextual and radiation data is combined using Scene Data Fusion (SDF) methods to better inform the reconstruction of the source radiation distribution from variations in the measured counts as the detector moves throughout the 3D environment. Here, we first present benchtop-scale characterization studies for both the neutron and gamma ray channels. In tandem, we present Geant4 simulations of both the single-crystal and full-system detection efficiencies over the omnidirectional field of view, and compare against validation measurements. We then demonstrate the imager's capabilities in a variety of different scenarios, ranging from free-moving handheld simultaneous measurements of Cs-137 and Cf-252 to more challenging motion-constrained or static measurement scenarios. In several of these scenarios we also demonstrate how the full omnidirectional multi-crystal responses markedly improve the reconstruction quality. The imager is therefore a promising system for conducting simultaneous gamma and neutron radiation measurements in applications such as homeland security, contamination mapping, and nuclear decommissioning.

physics.ins-det↗

arXiv2022

Design and deployment of radiological point-source arrays for the emulation of continuous distributed sources

We demonstrate a method for using arrays of point sources that emulate -- when measured from a standoff of at least several meters -- distributed gamma-ray sources, and present results using this method from outdoor aerial measurements of several planar arrays each comprising up to $100$ ${\sim}7$ mCi Cu-64 sealed sources. The method relies on the Poisson deviance to statistically test whether the array source ''looks like'' its continuous analogue to a particular gamma-ray detector given the counts recorded as the detector moves about 3D space. We use this deviance metric to design eight different mock distributed sources, ranging in complexity from a $36\times36$ m uniform square grid of sources to a configuration where regions of higher and zero activity are superimposed on a uniform baseline. We then detail the design, manufacture, and testing of the ${\sim} 7$ mCi Cu-64 sealed sources at the Washington State University research reactor, and their deployment during the aerial measurement campaign. We show the results of two such measurements, in which approximate source shapes and qualitative source intensities can be seen. Operationally, we find that the point-source array technique provides high source placement accuracy and ease of quantifying the true source configuration, scalability to source dimensions of ${\lesssim}100$ m, ease of reconfiguration and removal, and relatively low dose to personnel. Finally, we consider potential improvements and generalizations of the point-source array technique for future measurement campaigns.

physics.ins-det↗

arXiv2022

Mapping the Minimum Detectable Activities of Gamma-Ray Sources in a 3-D Scene

The ability to formulate maps of minimum detectable activities (MDAs) that describe the sensitivity of an ad hoc measurement that used one or more freely moving radiation detector systems would be significantly beneficial for the conduct and understanding of many radiological search activities. In a real-time scenario with a free-moving detector system, an MDA map can provide useful feedback to the operator about which areas have not been searched as thoroughly as others, thereby allowing the operator to prioritize future actions. Similarly, such a calculation could be used to inform subsequent navigation decisions of autonomous platforms. Here we describe a near real-time MDA mapping approach that can be applied when searching for point sources using detected events in a spectral region of interest while assuming a constant, unknown background rate. We show the application of this MDA mapping method to a real scenario, a survey of the interior of a small building using a handheld detector system. Repeated measurements with no sources and with Cs-137 sources of different strengths yield results consistent with the estimated thresholds and MDA values; namely, that for background-only measurements no sources are seen above threshold anywhere in the scene, while when sources are present they are detected above the thresholds calculated for their locations.

physics.ins-det↗

arXiv2021

Free-moving Quantitative Gamma-ray Imaging

The ability to map and estimate the activity of radiological source distributions in unknown three-dimensional environments has applications in the prevention and response to radiological accidents or threats as well as the enforcement and verification of international nuclear non-proliferation agreements. Such a capability requires well-characterized detector response functions, accurate time-dependent detector position and orientation data, a digitized representation of the surrounding 3D environment, and appropriate image reconstruction and uncertainty quantification methods. We have previously demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems on a relative intensity scale using a technique called Scene Data Fusion (SDF). Here we characterize the detector response of a multi-element gamma-ray imaging system using experimentally benchmarked Monte Carlo simulations and perform 3D mapping on an absolute intensity scale. We present experimental reconstruction results from hand-carried and airborne measurements with point-like and distributed sources in known configurations, demonstrating quantitative SDF in complex 3D environments.

physics.ins-det↗

arXiv2020

Reconstructing the Position and Intensity of Multiple Gamma-Ray Point Sources with a Sparse Parametric Algorithm

We present an experimental demonstration of Additive Point Source Localization (APSL), a sparse parametric imaging algorithm that reconstructs the 3D positions and activities of multiple gamma-ray point sources. Using a handheld gamma-ray detector array and up to four $8$ $μ$Ci $^{137}$Cs gamma-ray sources, we performed both source-search and source-separation experiments in an indoor laboratory environment. In the majority of the source-search measurements, APSL reconstructed the correct number of sources with position accuracies of ${\sim}20$ cm and activity accuracies (unsigned) of ${\sim}20\%$, given measurement times of two to three minutes and distances of closest approach (to any source) of ${\sim}20$ cm. In source-separation measurements where the detector could be moved freely about the environment, APSL was able to resolve two sources separated by $75$ cm or more given only ${\sim}60$ s of measurement time. In these source-separation measurements, APSL produced larger total activity errors of ${\sim}40\%$, but obtained source separation distances accurate to within $15$ cm. We also compare our APSL results against traditional Maximum Likelihood-Expectation Maximization (ML-EM) reconstructions, and demonstrate improved image accuracy and interpretability using APSL over ML-EM. These results indicate that APSL is capable of accurately reconstructing gamma-ray source positions and activities using measurements from existing detector hardware.

physics.ins-det↗