skip to main content
Idioma:
Tipo de recurso Mostra resultados com: Mostra resultados com: Índice

Machine learning n/γ discrimination in CLYC scintillators

Doucet, E. ; Brown, T. ; Chowdhury, P. ; Lister, C.J. ; Morse, C. ; Bender, P.C. ; Rogers, A.M.

Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, 2020-02, Vol.954 (C), p.161201, Article 161201 [Periódico revisado por pares]

Netherlands: Elsevier B.V

Texto completo disponível

Citações Citado por
  • Título:
    Machine learning n/γ discrimination in CLYC scintillators
  • Autor: Doucet, E. ; Brown, T. ; Chowdhury, P. ; Lister, C.J. ; Morse, C. ; Bender, P.C. ; Rogers, A.M.
  • Assuntos: [formula omitted]-means ; Artificial neural networks ; Cluster analysis ; CLYC ; Machine learning ; Pulse-shape discrimination
  • É parte de: Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, 2020-02, Vol.954 (C), p.161201, Article 161201
  • Notas: USDOE
    FG02-94ER40848
  • Descrição: Two machine learning techniques, one supervised (Artificial Neural Network) and the other unsupervised (k-means++) have been applied to the task of n/γ discrimination in 7Li-enriched CLYC detectors, and compared to traditional charge-comparison methods. The results show that a very basic artificial neural network can provide very good discrimination in the energy range investigated, and the k-means++ algorithm is capable of separating neutrons and gamma-rays in CLYC scintillators as well as suggesting reasonable window parameters for charge comparison methods.
  • Editor: Netherlands: Elsevier B.V
  • Idioma: Inglês
Links
Voltar para lista de resultados
Ir para página anteriorAnterior Resultado  19 AvançarIr para próxima página

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.