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Paricle identification at VAMOS++ with machine learning techniques

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Title
Paricle identification at VAMOS++ with machine learning techniques
Author(s)
Y. Cho; Yung Hee Kim; Choi, S.; Joochun Park; Sunghan Bae; Kevin Insik Hahn; Y. Son; Navin, A.; Lemasson, A.; Rejmund, M.; Ramos, D.; Ackermann, D.; Utepov, A.; Fourgeres, C.; Thomas, J.C.; Goupil, J.; Fremont, G.; de, France G.; 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.; 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.
Publication Date
2023-08
Journal
Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms, v.541, pp.240 - 242
Publisher
Elsevier B.V.
Abstract
Multi-nucleon transfer reaction between 136Xe beam and 198Pt target was performed using the VAMOS++ spectrometer at GANIL to study the structure of n-rich nuclei around N=126. Unambiguous charge state identification was obtained by combining two supervised machine learning methods, deep neural network (DNN) and positional correction using a gradient-boosting decision tree (GBDT). The new method reduced the complexity of the kinetic energy calibration and outperformed the conventional method improving the charge state resolution by 8%. © 2023
URI
https://pr.ibs.re.kr/handle/8788114/14712
DOI
10.1016/j.nimb.2023.05.053
ISSN
0168-583X
Appears in Collections:
Center for Exotic Nuclear Studies(희귀 핵 연구단) > 1. Journal Papers (저널논문)
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