Recovery of DESI BGS redshift measurements using machine learning
The DESI (Dark Energy Spectroscopic Instrument) project will conduct a five-year survey designed to cover 14.000 deg² by studying baryon acoustic oscillations (BAO) and redshift-space distortions (RSD). DESI needs simulations for its design, development, and operation, it uses simulations to evaluat...
- Autores:
-
Lobo Bolaño, Sergio David
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2019
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/39560
- Acceso en línea:
- http://hdl.handle.net/1992/39560
- Palabra clave:
- Galaxias
Análisis espectral
Aprendizaje automático (Inteligencia artificial)
Física
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-sa/4.0/
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Recovery of DESI BGS redshift measurements using machine learning |
| title |
Recovery of DESI BGS redshift measurements using machine learning |
| spellingShingle |
Recovery of DESI BGS redshift measurements using machine learning Galaxias Análisis espectral Aprendizaje automático (Inteligencia artificial) Física |
| title_short |
Recovery of DESI BGS redshift measurements using machine learning |
| title_full |
Recovery of DESI BGS redshift measurements using machine learning |
| title_fullStr |
Recovery of DESI BGS redshift measurements using machine learning |
| title_full_unstemmed |
Recovery of DESI BGS redshift measurements using machine learning |
| title_sort |
Recovery of DESI BGS redshift measurements using machine learning |
| dc.creator.fl_str_mv |
Lobo Bolaño, Sergio David |
| dc.contributor.advisor.none.fl_str_mv |
Forero Romero, Jaime Ernesto |
| dc.contributor.author.none.fl_str_mv |
Lobo Bolaño, Sergio David |
| dc.contributor.jury.none.fl_str_mv |
Sabogal Martínez, Beatriz Eugenia |
| dc.subject.keyword.es_CO.fl_str_mv |
Galaxias Análisis espectral Aprendizaje automático (Inteligencia artificial) |
| topic |
Galaxias Análisis espectral Aprendizaje automático (Inteligencia artificial) Física |
| dc.subject.themes.none.fl_str_mv |
Física |
| description |
The DESI (Dark Energy Spectroscopic Instrument) project will conduct a five-year survey designed to cover 14.000 deg² by studying baryon acoustic oscillations (BAO) and redshift-space distortions (RSD). DESI needs simulations for its design, development, and operation, it uses simulations to evaluate the data pipelines and measurements of redshifts from the spectrometers. In particular, DESI uses a simulated survey of mock galaxies to compare their redshifts with the -also simulated- redshift measurements of DESI. These redshift measurements, however, present differences with respect to the true redshifts values of the mock galaxies. It is necessary to correct these measurements so that the instrument can work properly when tested in the real world. The objective of this monograph is, therefore, to apply machine learning (ML) methods to the simulation data to recover the true redshift measurements of the Bright Galaxy Sample using observational variables as input. First, we pre-process the data and select |
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2019 |
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2019 |
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2020-06-10T16:20:44Z |
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2020-06-10T16:20:44Z |
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Trabajo de grado - Pregrado |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://hdl.handle.net/1992/39560 |
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u821698.pdf |
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repourl:https://repositorio.uniandes.edu.co/ |
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http://hdl.handle.net/1992/39560 |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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43 hojas |
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Universidad de los Andes |
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Física |
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Departamento de Física |
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Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Forero Romero, Jaime Ernestovirtual::14002-1Lobo Bolaño, Sergio Davidba7763c8-6abc-411c-8c41-46cd4de3ac06500Sabogal Martínez, Beatriz Eugenia2020-06-10T16:20:44Z2020-06-10T16:20:44Z2019http://hdl.handle.net/1992/39560u821698.pdfinstname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The DESI (Dark Energy Spectroscopic Instrument) project will conduct a five-year survey designed to cover 14.000 deg² by studying baryon acoustic oscillations (BAO) and redshift-space distortions (RSD). DESI needs simulations for its design, development, and operation, it uses simulations to evaluate the data pipelines and measurements of redshifts from the spectrometers. In particular, DESI uses a simulated survey of mock galaxies to compare their redshifts with the -also simulated- redshift measurements of DESI. These redshift measurements, however, present differences with respect to the true redshifts values of the mock galaxies. It is necessary to correct these measurements so that the instrument can work properly when tested in the real world. The objective of this monograph is, therefore, to apply machine learning (ML) methods to the simulation data to recover the true redshift measurements of the Bright Galaxy Sample using observational variables as input. First, we pre-process the data and select(traducción literal del resumen en inglés) El proyecto DESI (Instrumento espectroscópico de energía oscura) llevará a cabo una encuesta de cinco años diseñada para cubrir 14.000 deg² estudiando las oscilaciones acústicas en bariones y las distorsiones del espacio de desplazamiento al rojo (RSD). DESI necesita simulaciones para su diseño, desarrollo y operación, usa simulaciones para evaluar las tuberías de datos y las mediciones de los desplazamientos al rojo de los espectrómetros. En particular, DESI utiliza una encuesta simulada de galaxias simuladas para comparar sus desplazamientos al rojo con las mediciones de DESI que también son simuladas. Estas mediciones de desplazamiento al rojo, sin embargo, presentan diferencias con respecto a los verdaderos valores de desplazamiento al rojo de las galaxias simuladas. Es necesario corregir estas mediciones para que el instrumento pueda funcionar correctamente cuando se prueba en el mundo real. El objetivo de esta monografía es, por lo tanto, aplicar métodos de aprendizaje automático (ML)FísicoPregrado43 hojasapplication/pdfengUniversidad de los AndesFísicaFacultad de CienciasDepartamento de Físicainstname:Universidad de los Andesreponame:Repositorio Institucional SénecaRecovery of DESI BGS redshift measurements using machine learningTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fhttp://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TPGalaxiasAnálisis espectralAprendizaje automático (Inteligencia artificial)FísicaPublicationhttps://scholar.google.es/citations?user=TLTK6WgAAAAJvirtual::14002-10000-0002-2890-3725virtual::14002-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000337102virtual::14002-1d34cd5a0-50f5-42ea-825e-b51f5368f321virtual::14002-1d34cd5a0-50f5-42ea-825e-b51f5368f321virtual::14002-1TEXTu821698.pdf.txtu821698.pdf.txtExtracted texttext/plain62747https://repositorio.uniandes.edu.co/bitstreams/d7897881-8740-496d-9c47-590786e1fb41/download57c0727ad08693ea035b4be555bc585dMD54ORIGINALu821698.pdfapplication/pdf1780736https://repositorio.uniandes.edu.co/bitstreams/31b1ca9e-18fd-496f-b048-f28a3482ee9d/download5d4aee1bd40633e8e4ab68e233cf2032MD51THUMBNAILu821698.pdf.jpgu821698.pdf.jpgIM Thumbnailimage/jpeg6763https://repositorio.uniandes.edu.co/bitstreams/f373cb94-790c-4159-90b1-abc564696e8b/download124031d8146c509e64030c6a353cdd97MD551992/39560oai:repositorio.uniandes.edu.co:1992/395602024-03-13 15:05:12.078http://creativecommons.org/licenses/by-nc-sa/4.0/open.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.co |