Paricle identification at VAMOS++ with machine learning techniques
Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms(2023)
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%.
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Key words
VAMOS++,Machine learning,Multi-nucleon transfer reaction
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