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Colonna, M.

Publications and source records attributed to Colonna, M..

6Li as a three-body system in the (p, 3He) reaction at astrophysical energies

Several astrophysical processes are governed by the occurrence of nuclear reactions involving light nuclei at energies below the Coulomb barrier. Among other effects, their understanding is challenged by the appearance of clustered structures in the ground-state configuration of some of these nuclei, which may have a significant impact on the reaction cross section. In this contribution, we focus on the Li 6 (p, He 3 ) He 4 reaction, to probe the role of clustered configurations of Li 6 . In particular, we consider a three-body ab-initio calculation, based on the hyperspherical harmonics (HH) method, of the Li 6 wave function (WF), together with a more phenomenological three-body model. We observe that the HH WF entails a degree of clustering much larger than obtained from the phenomenological WFs. However, the corresponding reaction cross section, evaluated as a direct two-nucleon transfer in distorted-wave Born approximation, still follows the scaling with the clustering strength already pointed out in a previous work [1] and exhibits an energy trend very similar to that obtained with realistic phenomenological WFs. This opens up interesting perspectives towards constraining

Perrotta, S.↗

Preliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model

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: In this work, 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.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗