27 citations · 112 across the 17 of their papers we have counts for
17 papers
Machine learning methods for spectroscopic information recovery under ultrafast photon pileup
Jayson R. Vavrek, Thomas D. MacDonald, Yue Shi Lai
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…
Transferability of data-driven optimization results across multiple pixelated CdZnTe spectrometers
Thomas D. MacDonald, Hannah S. Parrilla, Jayson R. Vavrek
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 gamm…
Radiological mapping and uncertainty quantification by a fast Microcanonical Langevin Monte Carlo sampler
Lei Pan, Jaewon Lee, Brian J. Quiter +3
Radiological mapping plays a critical role in nuclear emergency response and environmental management activities. A radiation image, representing the spatial and intensity distribu…
Inter-detector differential fuzz testing for tamper detection in gamma spectrometers
Pei Yao Li, Jayson R. Vavrek, Sean Peisert
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 detec…
Data-driven optimization of pixelated CdZnTe spectrometers for uranium enrichment assay
Jayson R. Vavrek, Thomas D. MacDonald, Hannah S. Parrilla +3
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…
Data-driven performance optimization of gamma spectrometers with many channels
Jayson R. Vavrek, Hannah S. Parrilla, Gabriel Aversano +3
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-…