Caracterización e identificación de partículas usando la tecnología Skipper CCD
Dark matter constitutes a significant portion of the Universe, yet our understanding of it remains limited. In this work, we focus on characterizing the IRONMAN experimental setup at IFCA, designed for dark matter direct detection utilizing Skipper-CCD technology capable of counting electrons. Despi...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/35399 |
| Acceso en línea: | https://hdl.handle.net/10902/35399 |
| Access Level: | acceso abierto |
| Palabra clave: | Dark matter Skipper-CCD MOSKITA Hot columns Deep neural network Muon Materia oscura Columna caliente Red neuronal profunda |
| Sumario: | Dark matter constitutes a significant portion of the Universe, yet our understanding of it remains limited. In this work, we focus on characterizing the IRONMAN experimental setup at IFCA, designed for dark matter direct detection utilizing Skipper-CCD technology capable of counting electrons. Despite the characterisation, we were unable to obtain quality images for scientific analysis. As an alternative, we used images from the MOSKITA detector at the LHC to develop a deep neural network capable of classifying the different types of particles detected. In addition, this neural network will be implemented in the official software of the DAMIC-M collaboration for future studies. Finally, using this neural network we verified that the number of muons is not directly correlated with luminosity. |
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