| Autor: |
T A Nahool, A M Abdelmonem, M S Ali, A M Yasser |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
New Journal of Physics, Vol 26, Iss 9, p 093035 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1367-2630 |
| DOI: |
10.1088/1367-2630/ad4a21 |
| Popis: |
This study employed machine learning (ML) algorithms to predict the linear attenuation coefficients (LACs) of materials in inorganic scintillation detectors, which are crucial for evaluating self-shielding properties. Predictions from various ML models were compared with results from the Phy-X/PSD program across different photon energies. The Gradient Boosting Regressor (GBR) model was identified as the most accurate model, achieving a testing set accuracy of 96.40%. This research showcases the potential of ML for efficiently and accurately estimating LACs, with the GBR model showing promise for applications in radiation detection and material science. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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