Paricle 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; 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. Andreev; W. Korten; F. Recchia; G. de Angelis; R. Perez 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

doi:10.1016/j.nimb.2023.05.053

Paricle 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, 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. HirayamaS. Jeong, T. Niwase, H. Miyatake, P. Schury, M. Rosenbusch, K. Chae, C. Kim, S. Kim, G.M. Gu, M.J. Kim, P. John, A. Andreev, W. Korten, F. Recchia, G. de Angelis, R. Perez 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

Physics

Research output: Contribution to journal › Article › peer-review

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%.

Original language	Undefined/Unknown
Pages (from-to)	240-242
Number of pages	3
Journal	Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
Volume	541
Early online date	26 May 2023
DOIs	https://doi.org/10.1016/j.nimb.2023.05.053
Publication status	Published - 1 Aug 2023

Bibliographical note

Keywords

VAMOS++
Machine learning
Multi-nucleon transfer reaction

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10.1016/j.nimb.2023.05.053

EMIS2022_Paricle_Identification_of_VAMOS_data_with_Machine_Learning_Techniques
© 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.
Accepted author manuscript, 403 KBLicence: CC BY

Nuclear Physics Consolidated Grant 2021-2024
Jenkins, D. (Principal investigator), Andreyev, A. (Co-investigator), Bashkanov, M. (Co-investigator), Bentley, M. (Co-investigator), Dobaczewski, J. J. (Co-investigator), Laird, A. M. (Co-investigator), Paschalis, S. (Co-investigator), Pastore, A. (Co-investigator), Petri, M. (Co-investigator), Wadsworth, R. (Co-investigator), Watts, D. (Co-investigator) & Zachariou, N. (Co-investigator)
SCIENCE AND TECHNOLOGY FACILITIES COUNCIL (STFC)
1/10/21 → 31/03/25
Project: Research project (funded) › Research

Cite this

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. D., Watanabe, Y. X., ... Mukai, M. (2023). Paricle identification at VAMOS++ with machine learning techniques. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 541, 240-242. https://doi.org/10.1016/j.nimb.2023.05.053

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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%.",

keywords = "VAMOS++, Machine learning, Multi-nucleon transfer reaction",

author = "Y. Cho and Y.H. Kim and S. Choi and J. Park and S. Bae and K.I. Hahn and Y. Son and A. Navin and A. Lemasson and M. Rejmund and D. Ramos and D. Ackermann and A. Utepov and C. Fourgeres and J.C. Thomas and J. Goupil and G. Fremont and France, {G. de} and Y.X. Watanabe and Y. Hirayama and S. Jeong and T. Niwase and H. Miyatake and P. Schury and M. Rosenbusch and K. Chae and C. Kim and S. Kim and G.M. Gu and M.J. Kim and P. John and A. Andreev and W. Korten and F. Recchia and Angelis, {G. de} and Vidal, {R. Perez} and K. Rezynkina and J. Ha and F. Didierjean and P. Marini and D. Treasa and I. Tsekhanovich and J. Dudouet and S. Bhattacharyya and G. Mukherjee and R. Banik and S. Bhattacharya and M. Mukai",

note = "{\textcopyright} 2023 Published by Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the University{\textquoteright}s Research Publications and Open Access policy.",

year = "2023",

month = aug,

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doi = "10.1016/j.nimb.2023.05.053",

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Cho, Y, Kim, YH, Choi, S, Park, J, Bae, S, Hahn, KI, Son, Y, Navin, A, Lemasson, A, Rejmund, M, Ramos, D, Ackermann, D, Utepov, A, Fourgeres, C, Thomas, JC, Goupil, J, Fremont, G, France, GD, Watanabe, YX, Hirayama, Y, Jeong, S, Niwase, T, Miyatake, H, Schury, P, Rosenbusch, M, Chae, K, Kim, C, Kim, S, Gu, GM, Kim, MJ, John, P, Andreev, A, Korten, W, Recchia, F, Angelis, GD, Vidal, RP, Rezynkina, K, Ha, J, Didierjean, F, Marini, P, Treasa, D, Tsekhanovich, I, Dudouet, J, Bhattacharyya, S, Mukherjee, G, Banik, R, Bhattacharya, S & Mukai, M 2023, 'Paricle identification at VAMOS++ with machine learning techniques', Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, vol. 541, pp. 240-242. https://doi.org/10.1016/j.nimb.2023.05.053

TY - JOUR

T1 - Paricle identification at VAMOS++ with machine learning techniques

AU - Cho, Y.

AU - Kim, Y.H.

AU - Choi, S.

AU - Park, J.

AU - Bae, S.

AU - Hahn, K.I.

AU - Son, Y.

AU - Navin, A.

AU - Lemasson, A.

AU - Rejmund, M.

AU - Ramos, D.

AU - Ackermann, D.

AU - Utepov, A.

AU - Fourgeres, C.

AU - Thomas, J.C.

AU - Goupil, J.

AU - Fremont, G.

AU - France, G. de

AU - Watanabe, Y.X.

AU - Hirayama, Y.

AU - Jeong, S.

AU - Niwase, T.

AU - Miyatake, H.

AU - Schury, P.

AU - Rosenbusch, M.

AU - Chae, K.

AU - Kim, C.

AU - Kim, S.

AU - Gu, G.M.

AU - Kim, M.J.

AU - John, P.

AU - Andreev, A.

AU - Korten, W.

AU - Recchia, F.

AU - Angelis, G. de

AU - Vidal, R. Perez

AU - Rezynkina, K.

AU - Ha, J.

AU - Didierjean, F.

AU - Marini, P.

AU - Treasa, D.

AU - Tsekhanovich, I.

AU - Dudouet, J.

AU - Bhattacharyya, S.

AU - Mukherjee, G.

AU - Banik, R.

AU - Bhattacharya, S.

AU - Mukai, M.

PY - 2023/8/1

Y1 - 2023/8/1

N2 - 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%.

AB - 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%.

KW - VAMOS++

KW - Machine learning

KW - Multi-nucleon transfer reaction

U2 - 10.1016/j.nimb.2023.05.053

DO - 10.1016/j.nimb.2023.05.053

M3 - Article

SN - 0168-583X

VL - 541

SP - 240

EP - 242

JO - Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms

JF - Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms

ER -

Paricle identification at VAMOS++ with machine learning techniques

Abstract

Bibliographical note

Keywords

Access to Document

Projects

Nuclear Physics Consolidated Grant 2021-2024

Cite this