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Uncertainty analysis of signal deconvolution using a measured instrument response function.
Hartouni, E P; Beeman, B; Caggiano, J A; Cerjan, C; Eckart, M J; Grim, G P; Hatarik, R; Moore, A S; Munro, D H; Phillips, T; Sayre, D B.
Afiliação
  • Hartouni EP; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Beeman B; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Caggiano JA; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Cerjan C; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Eckart MJ; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Grim GP; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Hatarik R; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Moore AS; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Munro DH; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Phillips T; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
  • Sayre DB; Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
Rev Sci Instrum ; 87(11): 11D841, 2016 Nov.
Article em En | MEDLINE | ID: mdl-27910423
A common analysis procedure minimizes the ln-likelihood that a set of experimental observables matches a parameterized model of the observation. The model includes a description of the underlying physical process as well as the instrument response function (IRF). In the case investigated here, the National Ignition Facility (NIF) neutron time-of-flight (nTOF) spectrometers, the IRF is constructed from measurements and models. IRF measurements have a finite precision that can make significant contributions to determine the uncertainty estimate of the physical model's parameters. We apply a Bayesian analysis to properly account for IRF uncertainties in calculating the ln-likelihood function used to find the optimum physical parameters.
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Base de dados: MEDLINE Idioma: En Ano de publicação: 2016 Tipo de documento: Article
Buscar no Google
Base de dados: MEDLINE Idioma: En Ano de publicação: 2016 Tipo de documento: Article