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primary topic: Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique

Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique
type: AcademicArticle
type: Article
abstract: In this work, we proposed a new approach in generating nuclear data using machine learning techniques. This paper focused on generation of nuclear cross section for neutron induced-nuclear reaction on iridium isotopes (Ir-191) and tantalum isotopes (Ta-181) target, specifically 191Ir (n,p)191Os and 181Ta (n, 2n)180Ta using random forest algorithms. The input consists of experimental datasets obtained from EXOR and simulated datasets from TALYS 1.9. We found that the regression curve generated by our model is in good agreement with the evaluated nuclear data library ENDF/B-VII.0, which is set as the benchmark. This shows a potential in building a machine learning model for generating nuclear cross section data for both well studied and understudied nuclear reaction. © 2022 Elsevier Ltd
date: 2022
volume: 187
is part of: Applied Radiation and Isotopes
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

Elsevier Ltd
type: Organization

Applied Radiation and Isotopes
type: Collection
issn: 09698043

S. Ayub
type: Person
given name: S.
family name: Ayub

X.Y. Chew
type: Person
given name: X.Y.
family name: Chew

M.A.B. Hamid
type: Person
given name: M.A.B.
family name: Hamid

N.N. Shahrol Nidzam
type: Person
given name: N.N.
family name: Shahrol Nidzam

H.G. Beh
type: Person
given name: H.G.
family name: Beh

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primary topic: Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique