Ultra Fast Simulation Algorithmics for Industrial Applications in Muon Tomography using Generative Adversarial Networks
ABSTRACT: Muon tomography is a Non-Destructive Testing technique that consists in using cosmic muons as a probing tool, in order to generate images of objects from the information given by the interaction with the muons. The development and application of this techniques requires the production of c...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/29319 |
| Acceso en línea: | https://hdl.handle.net/10902/29319 |
| Access Level: | acceso abierto |
| Palabra clave: | Muon tomography Cosmic muons Generative adversarial networks Simulation Machine learning Tomografía muónica Muones cósmicos Redes generativas adversarias Simulación Aprendizaje automático |
| Sumario: | ABSTRACT: Muon tomography is a Non-Destructive Testing technique that consists in using cosmic muons as a probing tool, in order to generate images of objects from the information given by the interaction with the muons. The development and application of this techniques requires the production of considerable amounts of simulation data, usually generated with complex and slow particle simulation software. In this work, we explore the use of Generative Adversarial Networks (GAN) as a way of generating simulation data for muon tomography applications in a faster and less computationally expensive way. We have observed that GAN architectures can nicely reproduce the process of propagation of cosmic muons crossing material 50 times faster than other simulation software. |
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