Paricle identification at VAMOS++ with machine learning techniques
Langue
en
Communication dans un congrès
Ce document a été publié dans
Nucl.Instrum.Meth.B, Nucl.Instrum.Meth.B, 2022-10-03, Daejeon. 2023, vol. 541, p. 240-242
Résumé en anglais
Multi-nucleon transfer reaction between <sup loc="pre">136</sup>Xe beam and <sup loc="pre">198</sup>Pt target was performed using the VAMOS++ spectrometer at GANIL to study the structure of n-rich nuclei around N=126. ...Lire la suite >
Multi-nucleon transfer reaction between <sup loc="pre">136</sup>Xe beam and <sup loc="pre">198</sup>Pt 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%.< Réduire
Mots clés en anglais
VAMOS++
Machine learning
Multi-nucleon transfer reaction
Origine
Importé de halUnités de recherche