eprintid: 16434
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/64/34
datestamp: 2023-12-19 03:22:57
lastmod: 2023-12-19 03:22:57
status_changed: 2023-12-19 03:06:14
type: article
metadata_visibility: show
creators_name: Hamid, M.A.B.
creators_name: Beh, H.G.
creators_name: Shahrol Nidzam, N.N.
creators_name: Chew, X.Y.
creators_name: Ayub, S.
title: Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique
ispublished: pub
keywords: Decision trees; Iridium; Learning systems; Machine learning; Nuclear reactions; Tantalum, ENDF/B-VII.0; In-buildings; Machine learning techniques; New approaches; Nuclear cross sections; Nuclear data; Nuclear data library; Random forest algorithm; Regression curve; Simulated datasets, Isotopes, iridium; tantalum; isotope, Article; computer simulation; decision tree; human; machine learning; mathematical parameters; neutron; neutron radiation; random forest; simulation; machine learning, Iridium; Isotopes; Machine Learning; Neutrons; Tantalum
note: cited By 0
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
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132228257&doi=10.1016%2fj.apradiso.2022.110306&partnerID=40&md5=eb80b0ae70f27663a406b039a2f81917
id_number: 10.1016/j.apradiso.2022.110306
full_text_status: none
publication: Applied Radiation and Isotopes
volume: 187
refereed: TRUE
issn: 09698043
citation:   Hamid, M.A.B. and Beh, H.G. and Shahrol Nidzam, N.N. and Chew, X.Y. and Ayub, S.  (2022) Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique.  Applied Radiation and Isotopes, 187.   ISSN 09698043