Assimilating fission-code FIFRELIN using machine learning

Autor: Bazelaire Guillaume, Chebboubi Abdelhazize, Bernard David, Daniel Geoffrey, Blanchard Jean-Baptiste
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: EPJ Web of Conferences, Vol 294, p 03002 (2024)
Druh dokumentu: article
ISSN: 2100-014X
DOI: 10.1051/epjconf/202429403002
Popis: 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.
Databáze: Directory of Open Access Journals