| Issue |
EPJ Web Conf.
Volume 306, 2024
FUSION23 – International Conference on Heavy-Ion Collisions at Near-Barrier Energies
|
|
|---|---|---|
| Article Number | 01042 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/epjconf/202430601042 | |
| Published online | 18 October 2024 | |
https://doi.org/10.1051/epjconf/202430601042
Fission trajectory analysis using ML techniques
1 Tokyo Institute of Technology(Tokyo Tech), Meguro, Tokyo 152-8550, Japan
2 NAT Research Center, 3129-45 Hibara, Muramatsu, Tokai, Naka, Ibaraki 319-1112, Japan
* e-mail: mukobara.y.aa@m.titech.ac.jp
** e-mail: ishizuka.c.aa@m.titech.ac.jp
Published online: 18 October 2024
This research analyzed trajectories of nuclear fission leading to symmetric or assymmetric mass division, obtained by a four-dimensional Langevin-model, using machine learning models. A hybrid neural network, combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both of which were types of Recurrent Neural Networks (RNN), was utilized to classify whether each Langevin trajectory led to symmetric or asymmetric mass division. It was found that the current model could classify fate of these trajectories before reaching to the final destination (symmetric or assymmetric mode) with an accuracy of over 70%, clearly overestimating the asymmetric data.
© The Authors, published by EDP Sciences, 2024
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