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arxiv: 2311.07103 · v2 · pith:5WJ7CIVJnew · submitted 2023-11-13 · ⚛️ physics.ins-det · nucl-ex

Particle Identification at VAMOS++ with Machine Learning Techniques

Y. Cho , Y. H. Kim , S. Choi , J. Park , S. Bae , K. I. Hahn , Y. Son , A. Navin
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A. Lemasson M. Rejmund D. Ramos D. Ackermann A. Utepov C. Fourgeres J. C. Thomas J. Goupil G. Fremont G. de France Y. X. Watanabe Y. Hirayama S. Jeong T. Niwase H. Miyatake P. Schury M. Rosenbusch K. Chae C. Kim S. Kim G. M. Gu M. J. Kim P. John A. N. Andreyev W. Korten F. Recchia G. de Angelis R. M. P\'erez Vidal K. Rezynkina J. Ha F. Didierjean P. Marini D. Treasa I. Tsekhanovich J. Dudouet S. Bhattacharyya G. Mukherjee R. Banik S. Bhattacharya M. Mukai
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classification ⚛️ physics.ins-det nucl-ex
keywords chargeidentificationlearningmachinemethodstatevamosaround
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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%

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