Gamma ray spectrum inversion based on master-secondary encoder-decoder network

Gamma-ray diagnosis can detect the energy and spatial distribution of fast ions, as well as identify disruption signs. The detector's response to the gamma-ray spectrum involves complex mappings, requiring a fast and accurate spectrum reconstruction method. The challenge lies in the ill-conditi...

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Vydané v:Computer physics communications Ročník 315; s. 109688
Hlavní autori: Hu, Rundu, Liu, Jian, Zhou, Ruijie, Hu, Liqun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.10.2025
Predmet:
Gamma-ray
Ill-posed problem
Master-secondary encoder-decoder neural network
Plasma diagnostic
Spectrum inversion
Ill-posed problem
Master-secondary encoder-decoder neural network
Plasma diagnostic
Gamma-ray
Spectrum inversion
ISSN:0010-4655
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Popis
Shrnutí:Gamma-ray diagnosis can detect the energy and spatial distribution of fast ions, as well as identify disruption signs. The detector's response to the gamma-ray spectrum involves complex mappings, requiring a fast and accurate spectrum reconstruction method. The challenge lies in the ill-conditioned nature of spectrum inversion, where errors in measurement can significantly amplify the uncertainties of the inversion results. To solve this, additional information is needed, introducing non-linearity into the problem. Traditional approaches typically rely on iterative algorithms, such as linear regularization, maximum likelihood estimation method (ML-EM), and Gold deconvolution (Gold). Recently, neural networks have gained traction due to their strong capability in handling non-linear and highly ill-posed problems. In this paper, we present a method leveraging a master-secondary network structure that splits the spectrum inversion into two simpler sub-problems, improving outcomes beyond those of a single network. This network structure is verified suitable for solving highly ill-posed inversion problems and applying to gamma-ray spectrum reconstruction. Our method's accuracy is compared to ML-EM and Gold, demonstrating superior stability and effectiveness, particularly under high noise conditions, achieving a level suitable for practical applications. This method has been successfully applied to gamma-ray spectrum detection in the EAST tokamak facility. •Strong noise resistance and robustness.•The master-secondary network architecture is used, enabling the network to converge more quickly when processing complex data.•This method has been successfully applied to gamma-ray spectrum inversion at the EAST tokamak.•Applicable to a wider range of diagnostics fitting the similar mathematical form.
ISSN:0010-4655
DOI:10.1016/j.cpc.2025.109688