Machine Learning for neutron source distributions
In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle list, which is only required during the training stage of the machine learning model. Once the source distribution has been learned, the model is independent of the original particle list, allowing for further sampling in an efficient, rapid, and memory-costless manner. The performance of various generative models is evaluated, including a variational autoencoder, a normalizing flow, a generative adversarial network, and a denoising diffusion model. These ...
| Authors | Jose Ignacio Robledo, Norberto Schmidt, Klaus Lieutenant et al. |
|---|---|
| Primary category | physics.ins-det |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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