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Simulation of the SONGS Reactor Antineutrino Flux Using DRAGON

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 Added by Christopher Jones
 Publication date 2011
  fields Physics
and research's language is English
 Authors C. L. Jones




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For reactor antineutrino experiments, a thorough understanding of the fuel composition and isotopic evolution is of paramount importance for the extraction of $theta_{13}$. To accomplish these goals, we employ the deterministic lattice code DRAGON, and analyze the instantaneous antineutrino rate from the San Onofre Nuclear Generating Station (SONGS) Unit 2 reactor in California. DRAGONs ability to predict the rate for two consecutive fuel cycles is examined.



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Rising interest in nuclear reactors as a source of antineutrinos for experiments motivates validated, fast, and accessible simulations to predict reactor fission rates. Here we present results from the DRAGON and MURE simulation codes and compare them to other industry standards for reactor core modeling. We use published data from the Takahama-3 reactor to evaluate the quality of these simulations against the independently measured fuel isotopic composition. The propagation of the uncertainty in the reactor operating parameters to the resulting antineutrino flux predictions is also discussed.
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From the discovery of the neutrino to the precision neutrino oscillation measurements in KamLAND, nuclear reactors have proven to be an important source of antineutrinos. As their power and our knowledge of neutrino physics has increased, more sensitive measurements have become possible. The next generation of reactor antineutrino experiments require more detailed simulations of the reactor core. Many of the reactor simulation codes are proprietary which makes detailed studies difficult. Here we present the results of the open source DRAGON code and compare it to other industry standards for reactor modeling. We use published data from the Takahama reactor to determine the quality of the simulations. The propagation of the uncertainty to the antineutrino flux is also discussed.
133 - Steve Dye 2017
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