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Assimilating fission-code FIFRELIN using machine learning

Authors :
Bazelaire Guillaume
Chebboubi Abdelhazize
Bernard David
Daniel Geoffrey
Blanchard Jean-Baptiste
Source :
EPJ Web of Conferences, Vol 294, p 03002 (2024)
Publication Year :
2024
Publisher :
EDP Sciences, 2024.

Abstract

This paper presents work that has been done on the FIFRELIN Monte-Carlo code. The purpose of the code is to simulate the de-excitation process of fission fragments. Numerous quantity of insterest are calculated (mass yields, prompt particle spectra, mulitiplicities … ). Up to now the code relies on four free parameters which control the initial excitation and total angular momentum of fission fragment. Finding the good set of the free parameters is a diffucult task. In this work, we have developed an optimization algorithm based on Gaussian Process regression.

Subjects

Subjects :
Physics
QC1-999

Details

Language :
English
ISSN :
2100014X
Volume :
294
Database :
Directory of Open Access Journals
Journal :
EPJ Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.be36656829db48c8bdd305c265a9b29e
Document Type :
article
Full Text :
https://doi.org/10.1051/epjconf/202429403002