Issue |
EPJ Web Conf.
Volume 146, 2017
ND 2016: International Conference on Nuclear Data for Science and Technology
|
|
---|---|---|
Article Number | 04003 | |
Number of page(s) | 6 | |
Section | Fission Physics and Observables | |
DOI | https://doi.org/10.1051/epjconf/201714604003 | |
Published online | 13 September 2017 |
https://doi.org/10.1051/epjconf/201714604003
Sensitivity of the 252Cf(sf) neutron observables to the FREYA input yield functions Y(A, Z, TKE)
1 Nuclear Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
2 Nuclear Theory Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
3 Nuclear and Chemical Sciences Division, Lawrence Livermore National Laboratory, Livermore, California 94551, USA
4 Physics Department, University of California at Davis, Davis, California 95616, USA
a e-mail: JRandrup@LBL.gov
Published online: 13 September 2017
Within the framework of the fission event generator FREYA, we are studying the sensitivity of various neutron observables to the yield distribution Y (A,Z,TKE) used as input to the code. Concentrating on spontaneous fission of 252Cf, we have sampled a large number of different input yield functions based on χ2 fits to the experimental data on Y (A) and Y (TKE|A). For each of these input yield distributions, we then use FREYA to generate a large sample of complete fission events from which we extract a variety of neutron observables, including the multiplicity distribution, the associated correlation coefficients, and its factorial moments, the dependence of the mean neutron multiplicity on the total fragment kinetic energy TKE and on the fragment mass number A, the neutron energy spectrum, and the two-neutron angular correlation function. In this way, we can determine the variation of these observables resulting from the uncertainties in the experimental mesurements. The imposition of a constraint on the resulting mean neutron multiplicity reduces the variation of the calculated neutron observables and provides a means for shrinking the uncertainties associated with the measured data.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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