Detection of the Sunyaev-Zeldovich effect through Deep Learning techniques
The component separation of the microwave sky (i.e. recovering the different galactic foregrounds and the Cosmic Microwave Background, CMB) has important implications in both cosmology and astrophysics. It allows an accurate characterization of the CMB and the foregrounds, hence, a proper analysis o...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/22450 |
| Acceso en línea: | http://hdl.handle.net/10902/22450 |
| Access Level: | acceso abierto |
| Palabra clave: | Component separation Sunyaev-Zeldovich Galaxy clusters Cosmic Microwave Background Deep Learning Convolutional Neural Networks Cosmology Separación de componentes Cúmulos de galaxias Fondo Cósmico de Microondas Redes neuronales convolucionadas Cosmología |
| Sumario: | The component separation of the microwave sky (i.e. recovering the different galactic foregrounds and the Cosmic Microwave Background, CMB) has important implications in both cosmology and astrophysics. It allows an accurate characterization of the CMB and the foregrounds, hence, a proper analysis of the cosmological parameters and tests of several astrophysical theories. In this work, we present a new approach to the detection of the Sunyaev-Zeldovich effect (a secondary anisotropy of the CMB photons caused by the intra-galaxy clusters electron gas) using convolutional neural networks, CNNs, on multi frequency maps of the experiments dedicated to the CMB detection. We want to set the basis for a more detailed work, comparing the efficacy and efficiency of this new detection method with the usual multi-frequency filters methods. In this project we have trained a CNN with a data set, from simulations of the CMB, SZ emission, and Gaussian noise. This first model has successfully identified the frequency dependence of the SZ, allowing its detection. However, we have noticed an strange behaviour between the output of the network and the flux of the sources. We have some theories about it. |
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