Issue |
EPJ Web Conf.
Volume 239, 2020
ND 2019: International Conference on Nuclear Data for Science and Technology
|
|
---|---|---|
Article Number | 17006 | |
Number of page(s) | 5 | |
Section | Experimental Facilities, Equipment, Techniques and Methods | |
DOI | https://doi.org/10.1051/epjconf/202023917006 | |
Published online | 30 September 2020 |
https://doi.org/10.1051/epjconf/202023917006
NuDEX: A new nuclear γ-ray cascades generator
1 Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Spain
2 Instituto de Física Corpuscular, CSIC - Universidad de Valencia, Spain
* e-mail: emilio.mendoza@ciemat.es
Published online: 30 September 2020
Evaluated nuclear data libraries written in ENDF-6 format are used by Monte Carlo codes such as Geant4, MCNP6 or FLUKA for the transport of low energy neutrons (up to 20 MeV). The format in which the production of γ-rays after neutron induced reactions is provided do not allow, in general, to generate these γ-ray cascades in a correlated way. This prevents, among other things, energy conservation event by event, which is crucial in many applications. We have developed a code capable to generate correlated de-excitation γ-ray cascades using as much information as possible available in the RIPL-3 and ENSDF nuclear structure data libraries, among other useful information. The code follows the same philosophy of the DICEBOX or DEGEN codes. It generates the complete level scheme and branching ratios of the nucleus by using all the information experimentally known (known level scheme and known branching ratios) and completing the missing information with the most reliable statistical models. This code is able to generate automatically cascades for a large variety of nuclei (∼300) without requiring a specific input for each particular isotope. The code has been written in C++ language and can be integrated in the Geant4 simulation toolkit framework.
© The Authors, published by EDP Sciences, 2020
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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