Astronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovae

"Detection of transient events has become an important research subject in today's astronomy. To detect, report and study such phenomena, different informatics approaches have been proposed, among the most important of these are the image processing pipelines. Using the LSST Science Pipeli...

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Autores:
Reyes Gómez, Juan Pablo
Tipo de recurso:
Doctoral thesis
Fecha de publicación:
2019
Institución:
Universidad de los Andes
Repositorio:
Séneca: repositorio Uniandes
Idioma:
eng
OAI Identifier:
oai:repositorio.uniandes.edu.co:1992/41233
Acceso en línea:
http://hdl.handle.net/1992/41233
Palabra clave:
Gran Telescopio para Rastreos Sinópticos - Investigaciones
Procesamiento de imágenes - Investigaciones - Estudio de casos
SNANA (Programa para computador) - Investigaciones
Supernovas (Astronomía) - Detección - Investigaciones
Astronomía infrarroja - Investigaciones
Ingeniería
Rights
openAccess
License
https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf
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repository_id_str
dc.title.es_CO.fl_str_mv Astronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovae
title Astronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovae
spellingShingle Astronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovae
Gran Telescopio para Rastreos Sinópticos - Investigaciones
Procesamiento de imágenes - Investigaciones - Estudio de casos
SNANA (Programa para computador) - Investigaciones
Supernovas (Astronomía) - Detección - Investigaciones
Astronomía infrarroja - Investigaciones
Ingeniería
title_short Astronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovae
title_full Astronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovae
title_fullStr Astronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovae
title_full_unstemmed Astronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovae
title_sort Astronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovae
dc.creator.fl_str_mv Reyes Gómez, Juan Pablo
dc.contributor.advisor.none.fl_str_mv Fouchez, Dominique
Hernández Hoyos, Marcela
dc.contributor.author.none.fl_str_mv Reyes Gómez, Juan Pablo
dc.contributor.jury.none.fl_str_mv Forero Romero, Jaime Ernesto
Gris, Philippe
Ealet, Anne
Diaconu, Cristinel
Hernández Peñaloza, José Tiberio
dc.subject.keyword.es_CO.fl_str_mv Gran Telescopio para Rastreos Sinópticos - Investigaciones
topic Gran Telescopio para Rastreos Sinópticos - Investigaciones
Procesamiento de imágenes - Investigaciones - Estudio de casos
SNANA (Programa para computador) - Investigaciones
Supernovas (Astronomía) - Detección - Investigaciones
Astronomía infrarroja - Investigaciones
Ingeniería
dc.subject.armarc.es_CO.fl_str_mv Procesamiento de imágenes - Investigaciones - Estudio de casos
SNANA (Programa para computador) - Investigaciones
Supernovas (Astronomía) - Detección - Investigaciones
Astronomía infrarroja - Investigaciones
dc.subject.themes.none.fl_str_mv Ingeniería
description "Detection of transient events has become an important research subject in today's astronomy. To detect, report and study such phenomena, different informatics approaches have been proposed, among the most important of these are the image processing pipelines. Using the LSST Science Pipelines Stack (or Stack for short), a framework created by the Data Management Team of the Large Synoptic Survey Telescope, we have developed additions to one of these pipelines, focused on supernovae detection on the images from the Canada France Hawaii Telescope. We were able to run a complete pipeline using as input pre-calibrated exposures, performing an image subtraction and then select high quality candidates to be supernovae and transients. We obtained reasonable processing times by parallelizing most stages in the pipeline, and validated the Supernovae-Ia detection using data from the Supernovae Legacy Survey. Finally, we show a reduction of the overall number of source detections up to 80\% the amount in the base pipeline, and we report up to 95\% less light curve candidates, while preserving up to 85\% of Supernovae Ia with high signal present on the same period of time. We also present a simple method to label each detection per object, that allow us to show that the final light curve candidates have a high proportion of positive residuals which can greatly help other transient classification methods."--Tomado del Formato de Documento de Grado.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-09-03T08:12:22Z
dc.date.available.none.fl_str_mv 2020-09-03T08:12:22Z
dc.type.spa.fl_str_mv Trabajo de grado - Doctorado
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dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/1992/41233
dc.identifier.doi.none.fl_str_mv 10.57784/1992/41233
dc.identifier.pdf.none.fl_str_mv u830314.pdf
dc.identifier.instname.spa.fl_str_mv instname:Universidad de los Andes
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identifier_str_mv 10.57784/1992/41233
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dc.format.extent.es_CO.fl_str_mv 140 hojas
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dc.publisher.es_CO.fl_str_mv Uniandes
Aix-Marseille Université, Faculté des Sciences, Ecole Doctorale Physique et Sciences de la Matière
dc.publisher.program.es_CO.fl_str_mv Doctorado en Ingeniería
dc.publisher.faculty.es_CO.fl_str_mv Facultad de Ingeniería
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spelling Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autores.https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Fouchez, Dominiqueb9999750-b389-48f0-8aeb-09ae32200248500Hernández Hoyos, Marcelavirtual::8643-1Reyes Gómez, Juan Pablo11381500Forero Romero, Jaime ErnestoGris, PhilippeEalet, AnneDiaconu, CristinelHernández Peñaloza, José Tiberio2020-09-03T08:12:22Z2020-09-03T08:12:22Z2019http://hdl.handle.net/1992/4123310.57784/1992/41233u830314.pdfinstname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/"Detection of transient events has become an important research subject in today's astronomy. To detect, report and study such phenomena, different informatics approaches have been proposed, among the most important of these are the image processing pipelines. Using the LSST Science Pipelines Stack (or Stack for short), a framework created by the Data Management Team of the Large Synoptic Survey Telescope, we have developed additions to one of these pipelines, focused on supernovae detection on the images from the Canada France Hawaii Telescope. We were able to run a complete pipeline using as input pre-calibrated exposures, performing an image subtraction and then select high quality candidates to be supernovae and transients. We obtained reasonable processing times by parallelizing most stages in the pipeline, and validated the Supernovae-Ia detection using data from the Supernovae Legacy Survey. Finally, we show a reduction of the overall number of source detections up to 80\% the amount in the base pipeline, and we report up to 95\% less light curve candidates, while preserving up to 85\% of Supernovae Ia with high signal present on the same period of time. We also present a simple method to label each detection per object, that allow us to show that the final light curve candidates have a high proportion of positive residuals which can greatly help other transient classification methods."--Tomado del Formato de Documento de Grado."La detección de objetos transitorios se ha convertido en un tema importante de investigación en la astronomía de hoy. Para detectar, reportar y estudiar dichos fenómenos, diferentes aproximaciones han sido propuestos, entre ellos, el uso de pipelines de procesamiento de imágenes. Usando el LSST Science Pipelines Stack (o Stack), un framework creado por el equipo de datos del LSST, desarrollamos adiciones a uno de dichos pipelines encargado de la detección de supernovas de tipo IA en las imágenes del telescopio CFHT. Logramos correr un pipeline completo, con tiempos reducidos de procesamiento, logrando una reducción de hasta 80% del número de detección dudosas, reportando 95% curvas de luz candidatas y preservando hasta el 85% de las supernovae Ia con alta señal presente."--Tomado del Formato de Documento de Grado.Doctor en IngenieríaDoctorado140 hojasapplication/pdfengUniandesAix-Marseille Université, Faculté des Sciences, Ecole Doctorale Physique et Sciences de la MatièreDoctorado en IngenieríaFacultad de Ingenieríainstname:Universidad de los Andesreponame:Repositorio Institucional SénecaAstronomical image processing from large all-sky photometric surveys for the detection and measurement of type Ia supernovaeTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesishttp://purl.org/coar/resource_type/c_db06http://purl.org/coar/version/c_970fb48d4fbd8a85Texthttp://purl.org/redcol/resource_type/TDGran Telescopio para Rastreos Sinópticos - InvestigacionesProcesamiento de imágenes - Investigaciones - Estudio de casosSNANA (Programa para computador) - InvestigacionesSupernovas (Astronomía) - Detección - InvestigacionesAstronomía infrarroja - InvestigacionesIngenieríaPublicationhttps://scholar.google.es/citations?user=9nnSYmMAAAAJvirtual::8643-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000326453virtual::8643-130e973c9-1db4-4731-b61b-bc73c4aceecdvirtual::8643-130e973c9-1db4-4731-b61b-bc73c4aceecdvirtual::8643-1TEXTu830314.pdf.txtu830314.pdf.txtExtracted texttext/plain274063https://repositorio.uniandes.edu.co/bitstreams/cbd0764b-b6d6-4f96-98c8-1087e93ea640/download26d55434f6a5a25757181a5f09a7ede6MD54THUMBNAILu830314.pdf.jpgu830314.pdf.jpgIM Thumbnailimage/jpeg16677https://repositorio.uniandes.edu.co/bitstreams/943caac4-a2b1-4bf7-8761-4732a3e93248/downloadadfe4615b45923d6d219c6d19e7d6c0fMD55ORIGINALu830314.pdfapplication/pdf7761588https://repositorio.uniandes.edu.co/bitstreams/d26f8cd4-3b00-495c-a93a-93598755637a/download488a883df0082d6f0a841d6a7f3531f2MD511992/41233oai:repositorio.uniandes.edu.co:1992/412332024-08-26 15:23:06.928https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdfopen.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.co