Paricle identification at VAMOS++ with machine learning techniques

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%.

Metadata

Authors/Creators:

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:

Submitted: 24 February 2023
Accepted: 12 May 2023
Published (online): 26 May 2023
Published: 1 August 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:

Date Deposited:

05 Jun 2023 15:20

Last Modified:

06 Dec 2023 15:09

Published Version:

https://doi.org/10.1016/j.nimb.2023.05.053

Status:

Published

Refereed:

Yes

Identification Number:

https://doi.org/10.1016/j.nimb.2023.05.053

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Filename: EMIS2022_Paricle_Identification_of_VAMOS_data_with_Machine_Learning_Techniques.pdf

Description: EMIS2022_Paricle_Identification_of_VAMOS_data_with_Machine_Learning_Techniques

Licence: CC-BY 2.5

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