Speaker
Pablo Martinez Ruiz Del Arbol
(Universidad de Cantabria and CSIC (ES))
Description
Generative Adversarial Neural Networks (GANN) are used to simulate the multiple scattering of muons crossing matter. In previous works, a GANN was designed and trained, successfully predicting the angular and spatial deviation distributions including their correlations. In this work we show that GANNs can be so good at this task that correct POCA images can be reconstructed from their randomly generated samples.
Author
Pablo Martinez Ruiz Del Arbol
(Universidad de Cantabria and CSIC (ES))
