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

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Summary
This summary is machine-generated.

Deep learning can emulate nuclear interaction models for ion-therapy, significantly reducing computation time. This approach uses a Variational Auto-Encoder to speed up simulations of carbon-12 reactions.

Keywords:
Deep LearningHadron-therapyIon-therapyMonte Carlo simulationsNuclear reactions

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Area of Science:

  • Nuclear Physics
  • Computational Physics
  • Medical Physics

Background:

  • Accurate simulation of nuclear interactions is crucial for advancing ion-therapy treatments.
  • The Boltzmann-Langevin One Body (BLOB) model simulates heavy ion interactions but is computationally intensive.
  • Existing models require significant computation time, limiting their application in medical settings.

Purpose of the Study:

  • To develop a computationally efficient method for simulating nuclear interactions relevant to ion-therapy.
  • To emulate the BLOB model using a Deep Learning algorithm for faster simulations.
  • To assess the feasibility of using a Variational Auto-Encoder (VAE) for emulating nuclear reaction models.

Main Methods:

  • A Variational Auto-Encoder (VAE) was trained to reproduce the Probability Density Function (PDF) of nucleon distribution from the BLOB model.
  • The VAE was trained to emulate BLOB simulations of Carbon-12 interactions at 62 MeV/u.
  • The VAE's latent space was organized by impact parameter, with a classifier trained jointly.

Main Results:

  • The VAE successfully reproduced the nuclear interaction distributions generated by the BLOB model.
  • The computational time required for simulations using the VAE was negligible.
  • The VAE demonstrated a significant speed-up compared to the original BLOB model.

Conclusions:

  • Deep learning, specifically VAEs, can effectively emulate complex nuclear interaction models for ion-therapy applications.
  • This approach drastically reduces computation time, making advanced simulations feasible for medical use.
  • Future work includes integrating the VAE into the Geant4 toolkit for broader application.