Paricle identification at VAMOS++ with machine learning techniques

Cho, Y., Kim, Y.H., Choi, S. et al. (45 more authors) (2023) Paricle identification at VAMOS++ with machine learning techniques. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. pp. 240-242. ISSN 0168-583X

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Cho, Y.
  • Kim, Y.H.
  • Choi, S.
  • Park, J.
  • Bae, S.
  • Hahn, K.I.
  • Son, Y.
  • Navin, A.
  • Lemasson, A.
  • Rejmund, M.
  • Ramos, D.
  • Ackermann, D.
  • Utepov, A.
  • Fourgeres, C.
  • Thomas, J.C.
  • Goupil, J.
  • Fremont, G.
  • France, G. de
  • Watanabe, Y.X.
  • Hirayama, Y.
  • Jeong, S.
  • Niwase, T.
  • Miyatake, H.
  • Schury, P.
  • Rosenbusch, M.
  • Chae, K.
  • Kim, C.
  • Kim, S.
  • Gu, G.M.
  • Kim, M.J.
  • John, P.
  • Andreev, A. ORCID logo https://orcid.org/0000-0003-2828-0262
  • Korten, W.
  • Recchia, F.
  • Angelis, G. de
  • Vidal, R. Perez
  • Rezynkina, K.
  • Ha, J.
  • Didierjean, F.
  • Marini, P.
  • Treasa, D.
  • Tsekhanovich, I.
  • Dudouet, J.
  • Bhattacharyya, S.
  • Mukherjee, G.
  • Banik, R.
  • Bhattacharya, S.
  • Mukai, M.
Copyright, Publisher and Additional Information:

© 2023 Published by Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy.
Keywords: VAMOS++,Machine learning,Multi-nucleon transfer reaction
Dates:
  • Published: 1 August 2023
  • Published (online): 26 May 2023
  • Accepted: 12 May 2023
  • Submitted: 24 February 2023
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Physics (York)
Funding Information:
Funder
Grant number
SCIENCE AND TECHNOLOGY FACILITIES COUNCIL (STFC)
ST/V001035/1
Depositing User: Pure (York)
Date Deposited: 05 Jun 2023 15:20
Last Modified: 16 Oct 2024 19:13
Published Version: https://doi.org/10.1016/j.nimb.2023.05.053
Status: Published
Refereed: Yes
Identification Number: 10.1016/j.nimb.2023.05.053
Open Archives Initiative ID (OAI ID):

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