1. Particle Identification at VAMOS++ with Machine Learning Techniques
- Author
-
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., de France, G., Watanabe, Y. X., Hirayama, Y., Jeong, S., Niwase, T., Miyatake, H., Schury, P., Rosenbusch, M., Chae, K., Kim, C., Kim, S., Gu, G. M., Kim, M. J., John, P., Andreyev, A. N., Korten, W., Recchia, F., de Angelis, G., Vidal, R. M. Pérez, Rezynkina, K., Ha, J., Didierjean, F., Marini, P., Treasa, D., Tsekhanovich, I., Dudouet, J., Bhattacharyya, S., Mukherjee, G., Banik, R., Bhattacharya, S., and Mukai, M.
- Subjects
Physics - Instrumentation and Detectors ,Nuclear Experiment - 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%
- Published
- 2023
- Full Text
- View/download PDF