NIST Authors in **Bold**

Author(s): | Kevin J. Coakley; Dominic F. Vecchia; Daniel S. Hussey; David L. Jacobson; |
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Title: | Neutron Tomography of a Fuel Cell: Statistical Learning Implementation of a Penalized Likelihood Method |

Published: | October 15, 2013 |

Abstract: | At the NIST Neutron Imaging Facility, we collect neutron projection data for both the dry and wet states of a Proton-Exchange-Membrane (PEM) fuel cell. Transmitted neutrons captured in a scintillator doped with Lithium-6 produce scintillation light that is detected by an amorphous silicon detector. Based on joint analysis of the dry and wet state projection data, we reconstruct a residual neutron attenuation image with a penalized likelihood method. We use a Huber penalty function and select its regularization parameters by two-fold cross-validation. Before reconstruction, we transform the projection data so that the variance-to-mean ratio is approximately one. For the measured projection data, the Penalized likelihood method reconstruction is visually sharper than a reconstruction yielded by Filtered Back Projection. In an idealized simulation experiment, we demonstrate that the cross-validation procedure selects regularization parameters that yield a reconstruction that is nearly optimal according to a root mean-square prediction error criterion. |

Citation: | IEEE Transactions on Nuclear Science |

Volume: | 60 |

Pages: | pp. 3945 - 3953 |

Keywords: | Bayesian,cross-validation,fuel cell,Huber penalty,method of surrogates,neutron attenuation,neutron transmission tomography, penalized likelihood, PEM fuel cell, statistical learning, water density |

Research Areas: | Statistics |