Artificial Intelligence in astronomy: machine learning and deep learning approaches to DESI data
Documento final de tesis para recibir el grado de Doctorado en Ciencias - Física
- Autores:
-
Suárez Pérez, John Fredy
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/68996
- Acceso en línea:
- http://hdl.handle.net/1992/68996
- Palabra clave:
- Artificial Intelligence
Astrophysics
Cosmology
Machine learning
Deep learning
Física
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Artificial Intelligence in astronomy: machine learning and deep learning approaches to DESI data |
| title |
Artificial Intelligence in astronomy: machine learning and deep learning approaches to DESI data |
| spellingShingle |
Artificial Intelligence in astronomy: machine learning and deep learning approaches to DESI data Artificial Intelligence Astrophysics Cosmology Machine learning Deep learning Física |
| title_short |
Artificial Intelligence in astronomy: machine learning and deep learning approaches to DESI data |
| title_full |
Artificial Intelligence in astronomy: machine learning and deep learning approaches to DESI data |
| title_fullStr |
Artificial Intelligence in astronomy: machine learning and deep learning approaches to DESI data |
| title_full_unstemmed |
Artificial Intelligence in astronomy: machine learning and deep learning approaches to DESI data |
| title_sort |
Artificial Intelligence in astronomy: machine learning and deep learning approaches to DESI data |
| dc.creator.fl_str_mv |
Suárez Pérez, John Fredy |
| dc.contributor.advisor.none.fl_str_mv |
Forero Romero, Jaime Ernesto |
| dc.contributor.author.none.fl_str_mv |
Suárez Pérez, John Fredy |
| dc.contributor.jury.none.fl_str_mv |
Villaescusa Navarro, Francisco Sabogal Martínez, Beatriz Eugenia |
| dc.contributor.researchgroup.es_CO.fl_str_mv |
Grupo de investigación de Astrofísica |
| dc.subject.keyword.none.fl_str_mv |
Artificial Intelligence Astrophysics Cosmology Machine learning Deep learning |
| topic |
Artificial Intelligence Astrophysics Cosmology Machine learning Deep learning Física |
| dc.subject.themes.es_CO.fl_str_mv |
Física |
| description |
Documento final de tesis para recibir el grado de Doctorado en Ciencias - Física |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-08-01T16:28:42Z |
| dc.date.available.none.fl_str_mv |
2023-08-01T16:28:42Z |
| dc.date.issued.none.fl_str_mv |
2023-07-11 |
| dc.type.es_CO.fl_str_mv |
Trabajo de grado - Doctorado |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/acceptedVersion |
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10.57784/1992/68996 |
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instname:Universidad de los Andes |
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reponame:Repositorio Institucional Séneca |
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repourl:https://repositorio.uniandes.edu.co/ |
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http://hdl.handle.net/1992/68996 |
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10.57784/1992/68996 instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
| dc.language.iso.es_CO.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.es_CO.fl_str_mv |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Forero Romero, Jaime Ernestovirtual::3685-1Suárez Pérez, John Fredy26563600Villaescusa Navarro, FranciscoSabogal Martínez, Beatriz EugeniaGrupo de investigación de Astrofísica2023-08-01T16:28:42Z2023-08-01T16:28:42Z2023-07-11http://hdl.handle.net/1992/6899610.57784/1992/68996instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Documento final de tesis para recibir el grado de Doctorado en Ciencias - FísicaArtificial Intelligence (AI) has shown promise in advancing fundamental physics knowledge, from particle physics to cosmology. The significant advancements in AI over the last decade have been increasingly applied to solve problems in astronomy, primarily motivated by the large amount of data generated by state-of-the-art facilities. In this thesis, we explore to what extent AI techniques can be useful in tackling problems in observational cosmology. We focused our efforts on applying data mining, machine learning, and deep learning to handle and analyze the data coming from the ongoing observations of the Dark Energy Spectroscopic Instrument (DESI). DESI is an advanced spectroscopic experiment that has been operational since 2020 and aims to build the most detailed 3D map of the Universe. DESI is a massive undertaking, and over the course of five years, it will measure approximately 40 million spectra from stars, galaxies, and quasars, generating an enormous amount of data that can benefit from advanced AI techniques for analysis and interpretation. In this thesis, we successfully achieved using AI techniques in three important aspects for DESI: 1) assessing the quality of the data generated by the experiment, 2) describing the cosmic web pattern on the DESI maps, and 3) predicting the redshift observed by DESI from the features observed in imaging data. These three achievements will help improve our understanding of the Universe's evolution and the nature of dark energy, not only with the data coming from DESI but also from future facilities and experiments.JFSP and JEFR acknowledge the support of the INV-2022-133-2337, INV-2022-137-2394, and INV-2021-126-2256 projects of the Universidad de Los Andes, Facultad de Ciencias.JFSP and JEFR acknowledge the support by the LACEGAL network with support from the European Union¿s Horizon 2020 Research and Innovation programme under the Marie Sklodowska-Curie grant agreement number 734374.Doctor en Ciencias - FísicaDoctoradoAstrofísica y cosmología computacional164 páginasapplication/pdfengUniversidad de los AndesDoctorado en Ciencias - FísicaFacultad de CienciasDepartamento de FísicaArtificial Intelligence in astronomy: machine learning and deep learning approaches to DESI dataTrabajo de grado - Doctoradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPArtificial IntelligenceAstrophysicsCosmologyMachine learningDeep learningFísicaAbbott, B. 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