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Activity Number: 309 - Bayesian Modeling in Physical Sciences and Engineering
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #330565 Presentation
Title: Neutron Capture Cross Sections for Unstable Nuclei with Surrogate Reaction Data
Author(s): Kassie Fronczyk* and Jutta Escher
Companies: Lawrence Livermore National Laboratory and Lawrence Livermore National Laboratory
Keywords: Nuclear Physics; Surrogate Rections; Cross Sections; Bayesian Modeling

Neutron capture reactions play an important role in nuclear physics to understand physical processes in which neutrons react with their environment. However these reactions can rarely be measured directly. We use a method for extracting cross sections for neutron-capture on unstable isotopes from indirect (surrogate) measurements. The compound-nucleus formation via the surrogate reaction is described using theoretical underpinnings, then we leverage all available information with a Bayesian model for the subsequent decay. The comparison of measured and predicted coincidence data allows for the determination of parameters needed to calculate the unknown cross section.

LLNL-ABS-749458 This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Authors who are presenting talks have a * after their name.

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