Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learning
This is a work that implements spectra of distant celestial objects taken by the spectrograph of the DESI collaboration. It seeks to deal with the current problem of ambiguity in the classification of Seyfert-type galaxies, causars and stars. For this purpose, a set of spectrophotometric variables i...
- Autores:
-
Fajardo Poveda, Daniel Andrés
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/74561
- Acceso en línea:
- https://hdl.handle.net/1992/74561
- Palabra clave:
- Astronomia
Extragalactica
Galaxias
Quasares
Estrellas
Clasificadores
Fotometria
Espectroscopia
Espectros
DESI
Machine learning
Redshift
Física
- Rights
- openAccess
- License
- Attribution-NoDerivatives 4.0 International
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| dc.title.eng.fl_str_mv |
Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learning |
| dc.title.alternative.spa.fl_str_mv |
Mapeando el Universo: clasificación de galaxias y cuásares a través de variables espectrales esenciales con aprendizaje automático |
| title |
Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learning |
| spellingShingle |
Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learning Astronomia Extragalactica Galaxias Quasares Estrellas Clasificadores Fotometria Espectroscopia Espectros DESI Machine learning Redshift Física |
| title_short |
Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learning |
| title_full |
Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learning |
| title_fullStr |
Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learning |
| title_full_unstemmed |
Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learning |
| title_sort |
Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learning |
| dc.creator.fl_str_mv |
Fajardo Poveda, Daniel Andrés |
| dc.contributor.advisor.none.fl_str_mv |
García Varela, José Alejandro |
| dc.contributor.author.none.fl_str_mv |
Fajardo Poveda, Daniel Andrés |
| dc.contributor.jury.none.fl_str_mv |
Sabogal Martínez, Beatriz Eugenia |
| dc.contributor.researchgroup.none.fl_str_mv |
Facultad de Ciencias |
| dc.subject.keyword.spa.fl_str_mv |
Astronomia |
| topic |
Astronomia Extragalactica Galaxias Quasares Estrellas Clasificadores Fotometria Espectroscopia Espectros DESI Machine learning Redshift Física |
| dc.subject.keyword.none.fl_str_mv |
Extragalactica Galaxias Quasares Estrellas Clasificadores Fotometria Espectroscopia Espectros DESI Machine learning Redshift |
| dc.subject.themes.spa.fl_str_mv |
Física |
| description |
This is a work that implements spectra of distant celestial objects taken by the spectrograph of the DESI collaboration. It seeks to deal with the current problem of ambiguity in the classification of Seyfert-type galaxies, causars and stars. For this purpose, a set of spectrophotometric variables is proposed and the best models for the creation of spectral classifiers are found. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-07-16T19:42:13Z |
| dc.date.available.none.fl_str_mv |
2024-07-16T19:42:13Z |
| dc.date.issued.none.fl_str_mv |
2024-07-16 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
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Text |
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http://purl.org/redcol/resource_type/TP |
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https://hdl.handle.net/1992/74561 |
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reponame:Repositorio Institucional Séneca |
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instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
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eng |
| language |
eng |
| dc.relation.references.none.fl_str_mv |
Adam, G. 2022, AJ, 163, 11 Adame, A. G. et al. 2023, arXiv preprint: [2306.06308] Chaussidon, E. et al. 2023, ApJ, 944, 107 Cárdenas, C. & Fajardo, D. 2023, Theoretical Project Report, Universidad de los Andes Farr, J. et al. 2020, Journal of Cosmology and Astroparticle Physics, 2020, 15 Gallagher, S. C. et al. 2015, RAS, 451, 2991 Ginsburg, A. et al. 2022, ApJ, 163, 291 Karttunen, H. et al. 2007, Fundamental Astronomy, Springer Li, R. et al. 2019, MNRAS, 482, 313 Li, Z. et al. 2022, MNRAS, 517, 4875 Myers, A. D. et al. 2023, Astron. J, 165, 50 Ness, M. et al. 2015, ApJ, 808, 16 Ostlie, A. & Carroll, W. 1996, Modern Stellar Astrophysics, Addison-Wesley Publishing Company Robitaille, T. P. et al. 2013, A&A, 558, A33 Saavedra, J. 2019, Undergraduate Thesis, Universidad de los Andes, 59 Wang, B. et al. 2022, ApJS, 259, 28 Zakamska, N. & Alexandroff, R. 2023, arXiv preprint: [2306.06303] |
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Attribution-NoDerivatives 4.0 International |
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Attribution-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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90 páginas |
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application/pdf |
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Universidad de los Andes |
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Física |
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Facultad de Ciencias |
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Departamento de Física |
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Universidad de los Andes |
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Universidad de los Andes |
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García Varela, José Alejandrovirtual::18957-1Fajardo Poveda, Daniel AndrésSabogal Martínez, Beatriz Eugeniavirtual::18958-1Facultad de Ciencias2024-07-16T19:42:13Z2024-07-16T19:42:13Z2024-07-16https://hdl.handle.net/1992/74561instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/This is a work that implements spectra of distant celestial objects taken by the spectrograph of the DESI collaboration. It seeks to deal with the current problem of ambiguity in the classification of Seyfert-type galaxies, causars and stars. For this purpose, a set of spectrophotometric variables is proposed and the best models for the creation of spectral classifiers are found.This thesis focused on resolving the current ambiguity in the classification of galaxies, quasars, and stars. There are some classifiers like RedRock or QuasarNET that perform spectral classifications and redshift measurements for the targets observed by DESI (Dark Energy Spectroscopic Instrument). However, not all results obtained for all types of targets are satisfactory; there are ambiguous limits between the spectral characteristics of Seyfert-type galaxies, quasars, and some high-temperature stars. This complicates and biases the analysis of experimental data. In this project, the construction of spectral classifiers was carried out, which, using a set of spectrophotometric variables (proposed by us), are capable of resolving the ambiguity problem in the classification of galaxies, stars, and quasars reported by the DESI collaboration when using the presence of spectral emission lines as a determining parameter to establish the labels of spectra with redshifts less than 2.1. With the tools developed here, it is expected to contribute to the massive processing of the spectra taken by the DESI collaboration, considering and dealing with the ambiguity in the distinction of galaxies, quasars, and stars. Additionally, it is expected to contribute to the understanding of the physical nature of quasars.PregradoExtragalactic spectroscopy90 páginasapplication/pdfengUniversidad de los AndesFísicaFacultad de CienciasDepartamento de FísicaAttribution-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Mapping the Universe: classifying galaxies and quasars through essential spectral features with machine learningMapeando el Universo: clasificación de galaxias y cuásares a través de variables espectrales esenciales con aprendizaje automáticoTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPAstronomiaExtragalacticaGalaxiasQuasaresEstrellasClasificadoresFotometriaEspectroscopiaEspectrosDESIMachine learningRedshiftFísicaAdam, G. 2022, AJ, 163, 11Adame, A. G. et al. 2023, arXiv preprint: [2306.06308]Chaussidon, E. et al. 2023, ApJ, 944, 107Cárdenas, C. & Fajardo, D. 2023, Theoretical Project Report, Universidad de los AndesFarr, J. et al. 2020, Journal of Cosmology and Astroparticle Physics, 2020, 15Gallagher, S. C. et al. 2015, RAS, 451, 2991Ginsburg, A. et al. 2022, ApJ, 163, 291Karttunen, H. et al. 2007, Fundamental Astronomy, SpringerLi, R. et al. 2019, MNRAS, 482, 313Li, Z. et al. 2022, MNRAS, 517, 4875Myers, A. D. et al. 2023, Astron. J, 165, 50Ness, M. et al. 2015, ApJ, 808, 16Ostlie, A. & Carroll, W. 1996, Modern Stellar Astrophysics, Addison-Wesley Publishing CompanyRobitaille, T. P. et al. 2013, A&A, 558, A33Saavedra, J. 2019, Undergraduate Thesis, Universidad de los Andes, 59Wang, B. et al. 2022, ApJS, 259, 28Zakamska, N. & Alexandroff, R. 2023, arXiv preprint: [2306.06303]202022617Publication88a1271b-7c5b-4cba-a02a-87878aba01e4virtual::18957-188a1271b-7c5b-4cba-a02a-87878aba01e4virtual::18957-11d39bc41-f9d3-461b-a7cc-84296451800fvirtual::18958-11d39bc41-f9d3-461b-a7cc-84296451800f1d39bc41-f9d3-461b-a7cc-84296451800fvirtual::18958-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000382418virtual::18957-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000382310virtual::18958-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000382310ORIGINALautorizacion tesis_signed.pdfautorizacion tesis_signed.pdfHIDEapplication/pdf413216https://repositorio.uniandes.edu.co/bitstreams/722c98b7-8d80-444a-af7f-33df92046ecd/download27598f16c678eb476d46184ed5995aedMD51Mapping_the_universe_.pdfMapping_the_universe_.pdfapplication/pdf6734552https://repositorio.uniandes.edu.co/bitstreams/af434a61-e669-4ca8-99b2-76fa4762c377/downloadb32dd4560b3de3d63fdfa7ff3935fcb5MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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