A machine learning approach for correcting glow curve anomalies in CaSO4:Dy-based TLD dosimeters used in personnel monitoring.
J Radiol Prot
; 43(3)2023 07 13.
Article
em En
| MEDLINE
| ID: mdl-37402358
ABSTRACT
The study presents a novel approach to analysing the thermoluminescence (TL) glow curves (GCs) of CaSO4Dy-based personnel monitoring dosimeters using machine learning (ML). This study demonstrates the qualitative and quantitative impact of different types of anomalies on the TL signal and trains ML algorithms to estimate correction factors (CFs) to account for these anomalies. The results show a good degree of agreement between the predicted and actual CFs, with a coefficient of determination greater than 0.95, a root mean square error less than 0.025, and a mean absolute error less than 0.015. The use of ML algorithms leads to a significant two-fold reduction in the coefficient of variation of TL counts from anomalous GCs. This study proposes a promising approach to address anomalies caused by dosimeter, reader, and handling-related factors. Furthermore, it accounts for non-radiation-induced TL at low dose levels towards improving the dosimetric accuracy in personnel monitoring.
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Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Dosimetria Termoluminescente
/
Dosímetros de Radiação
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article