Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model
A. Ciardiello🇮🇹 · M. Asai🇺🇸 · B. Caccia🇮🇹 · G.A.P. Cirrone🇮🇹 · M. Colonna🇮🇹 · A. Dotti🇺🇸 · R. Faccini🇮🇹 · S. Giagu🇮🇹 · A. Messina🇮🇹 · P. Napolitani🇫🇷 · L. Pandola🇮🇹 · D.H. Wright🇺🇸 · C. Mancini-Terracciano🇮🇹
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 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 C with 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 () training a classifier of 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.