Use of generative adversarial neural networks in scattering muography
DOI:
https://doi.org/10.31526/JAIS.2024.487Keywords:
Muography, GAN networks, Machine Learning MuographyAbstract
Many muography applications make an extensive use of simulations to determine detectors design or to train imaging or regression algorithms. The computing cost of producing these simulations is usually quite high, specially concerning the interaction of cosmic muons with matter. This work explores the possibility of using Generative Adversarial Neural (GAN) networks to produce a fast and realistic simulation of the multiple scattering process. The results of the network are confronted with GEANT4 simulations using a benchmark problem related to the measurement of the inner wear of industrial pipes. The GAN is able to reproduce the angular distributions and correlations with a speed up factor of roughly 50 with
respect to GEANT4.
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