Generation of cross section for neutron induced nuclear reaction on iridium and tantalum isotope using machine learning technique
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...
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| Main Authors: | , , , , |
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| Format: | Article |
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Elsevier Ltd
2022
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132228257&doi=10.1016%2fj.apradiso.2022.110306&partnerID=40&md5=eb80b0ae70f27663a406b039a2f81917 http://eprints.utp.edu.my/33517/ |
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| Summary: | 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 |
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