Data-driven optimization of pixelated CdZnTe spectrometers for uranium enrichment assay
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917126804078592 |
|---|---|
| author | Vavrek, Jayson R. MacDonald, Thomas D. Parrilla, Hannah S. Deshmukh, Nikhil S. Zalavadia, Mital A. McDonald, Benjamin S. |
| author_facet | Vavrek, Jayson R. MacDonald, Thomas D. Parrilla, Hannah S. Deshmukh, Nikhil S. Zalavadia, Mital A. McDonald, Benjamin S. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18517 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven optimization of pixelated CdZnTe spectrometers for uranium enrichment assay Vavrek, Jayson R. MacDonald, Thomas D. Parrilla, Hannah S. Deshmukh, Nikhil S. Zalavadia, Mital A. McDonald, Benjamin S. Instrumentation and Detectors 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. |
| title | Data-driven optimization of pixelated CdZnTe spectrometers for uranium enrichment assay |
| topic | Instrumentation and Detectors |
| url | https://arxiv.org/abs/2509.18517 |