تقرير
Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model
| العنوان: | Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model |
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| المؤلفون: | Ciardiello, A., Asai, M., Caccia, B., Cirrone, G. A. P., Colonna, M., Dotti, A., Faccini, R., Giagu, S., Messina, A., Napolitani, P., Pandola, L., Wright, D. H., Mancini-Terracciano, C. |
| المصدر: | Physica Medica Volume 73, May 2020, Pages 65-72 |
| سنة النشر: | 2020 |
| المجموعة: | Nuclear Experiment Physics (Other) |
| مصطلحات موضوعية: | Physics - Computational Physics, Nuclear Experiment, Physics - Medical Physics |
| الوصف: | Purpose: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB ("Boltzmann-Langevin One Body"), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate also $^{12}$C reactions in the same energy domain. However, its computation time is too long for any medical application. For this reason we present the possibility of emulating it with a Deep Learning algorithm. Methods: The BLOB final state is a Probability Density Function (PDF) of finding a nucleon in a position of the phase space. We discretised this PDF and trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a proof of concept, we developed and trained a VAE to emulate BLOB in simulating the interactions of $^{12}$C with $^{12}$C at 62 MeV/u. To have more control on the generation, we forced the VAE latent space to be organised with respect to the impact parameter ($b$) training a classifier of $b$ jointly with the VAE. Results: The distributions obtained from the VAE are similar to the input ones and the computation time needed to use the VAE as a generator is negligible. Conclusions: We show that it is possible to use a Deep Learning approach to emulate a model developed to simulate nuclear reactions in the energy range of interest for Ion-therapy. We foresee the implementation of the generation part in C++ and to interface it with the most used Monte Carlo toolkit: Geant4. Comment: 8 pages, 9 figures, Accepted by Physica Medica |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.ejmp.2020.04.005 |
| URL الوصول: | http://arxiv.org/abs/2004.04961 |
| رقم الأكسشن: | edsarx.2004.04961 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.ejmp.2020.04.005 |
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