Improving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run

Esta investigación desarrolla una herramienta para clasificar ondas gravitacionales (GWs) de ruido de detector para informar la aceptación/retractación de eventos de GWs para la colaboración LIGO-Virgo-KAGRA.

Autores:
Álvarez López, María Sofía
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:1992/68738
Acceso en línea:
http://hdl.handle.net/1992/68738
Palabra clave:
LIGO
Ondas gravitacionales
Gravitational-waves
Gravitational-wave detector characterization
Machine Learning
Ground-based gravitational-wave interferometers
Física
Rights
openAccess
License
Attribution-NoDerivatives 4.0 Internacional
id UNIANDES2_d6b68351eb749cd7835152b7f4e52606
oai_identifier_str oai:repositorio.uniandes.edu.co:1992/68738
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network_name_str Séneca: repositorio Uniandes
repository_id_str
dc.title.none.fl_str_mv Improving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run
title Improving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run
spellingShingle Improving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run
LIGO
Ondas gravitacionales
Gravitational-waves
Gravitational-wave detector characterization
Machine Learning
Ground-based gravitational-wave interferometers
Física
title_short Improving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run
title_full Improving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run
title_fullStr Improving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run
title_full_unstemmed Improving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run
title_sort Improving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run
dc.creator.fl_str_mv Álvarez López, María Sofía
dc.contributor.advisor.none.fl_str_mv McIver, Jess
García Varela, José Alejandro
dc.contributor.author.none.fl_str_mv Álvarez López, María Sofía
dc.contributor.jury.none.fl_str_mv Chan, Man Leong (Mervyn)
dc.contributor.researchgroup.es_CO.fl_str_mv Astronomía y astrofísica
dc.subject.keyword.none.fl_str_mv LIGO
Ondas gravitacionales
Gravitational-waves
Gravitational-wave detector characterization
Machine Learning
Ground-based gravitational-wave interferometers
topic LIGO
Ondas gravitacionales
Gravitational-waves
Gravitational-wave detector characterization
Machine Learning
Ground-based gravitational-wave interferometers
Física
dc.subject.themes.es_CO.fl_str_mv Física
description Esta investigación desarrolla una herramienta para clasificar ondas gravitacionales (GWs) de ruido de detector para informar la aceptación/retractación de eventos de GWs para la colaboración LIGO-Virgo-KAGRA.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-07-25T18:20:57Z
dc.date.available.none.fl_str_mv 2023-07-25T18:20:57Z
dc.date.issued.none.fl_str_mv 2023-07-12
dc.type.es_CO.fl_str_mv Trabajo de grado - Pregrado
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.version.none.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.coar.none.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.content.es_CO.fl_str_mv Text
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format http://purl.org/coar/resource_type/c_7a1f
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/1992/68738
dc.identifier.instname.es_CO.fl_str_mv instname:Universidad de los Andes
dc.identifier.reponame.es_CO.fl_str_mv reponame:Repositorio Institucional Séneca
dc.identifier.repourl.es_CO.fl_str_mv repourl:https://repositorio.uniandes.edu.co/
url http://hdl.handle.net/1992/68738
identifier_str_mv 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
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spelling Attribution-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2McIver, Jessbcf5afdb-1fd2-49b1-88a0-5ac985000ee3600García Varela, José Alejandrovirtual::9830-1Álvarez López, María Sofía689609b1-ed8f-494f-b3c1-83538435b87e600Chan, Man Leong (Mervyn)Astronomía y astrofísica2023-07-25T18:20:57Z2023-07-25T18:20:57Z2023-07-12http://hdl.handle.net/1992/68738instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Esta investigación desarrolla una herramienta para clasificar ondas gravitacionales (GWs) de ruido de detector para informar la aceptación/retractación de eventos de GWs para la colaboración LIGO-Virgo-KAGRA.A pesar de haber alcanzado sensibilidades capaces de detectar la amplitud extremadamente pequeña de las ondas gravitacionales (GWs), los datos de los detectores LIGO y Virgo contienen frecuentes ráfagas de ruido transitorio no Gaussiano, comúnmente conocidas como "glitches". Los "glitches" se presentan en diversas morfologías de tiempo-frecuencia, y resultan especialmente problemáticos cuando imitan la forma de las GWs reales. Dada la mayor tasa de eventos esperada en el actual periodo de observación de LIGO-Virgo (O4), la validación de los candidatos de eventos de GWs requiere mayores niveles de automatización. Gravity Spy, una herramienta de aprendizaje automático que clasificó con éxito tipos comunes de "glitches" de LIGO y Virgo en observaciones anteriores, tiene el potencial de ser reestructurada como un clasificador de señales de GWs-vs-ruido de detector para distinguir entre "glitches" y señales de GW con precisión. Un clasificador de señales de GWs-vs-"glitches" utilizado para la automatización debe ser robusto y compatible con una amplia gama de ruido de fondo, nuevas fuentes de "glitches" y la probable aparición de "glitches" y GWs solapados en la misma ventana de tiempo. Presentamos GSpyNetTree, el Gravity Spy Convolutional Neural Network Decision Tree: un clasificador multi-etiqueta multi-CNN que utiliza CNNs en un árbol de decisión ordenado a través de la masa total de un evento candidato de onda gravitacional. Integrado en el Informe de Calidad de Datos de LIGO-Virgo (DQR, por sus siglas en inglés), GSpyNetTree es una de las herramientas esenciales en la evaluación de la necesidad de mitigación de "glitches" en O4. Esta tesis presenta el desarrollo de GSpyNetTree, su construcción y resultados, desde su origen como un clasificador multi-clase a su estado actual como clasificador multi-etiqueta. Por último, se evalúa su desempeño en candidatos de ondas gravitacionales del actual periodo de observación, O4, y se proponen técnicas para mejorar su desempeño en futuras iteraciones.Despite achieving sensitivities capable of detecting the extremely small amplitude of gravitational waves (GWs), LIGO and Virgo detector data contain frequent bursts of non-Gaussian transient noise, commonly known as 'glitches'. Glitches come in various time-frequency morphologies, and they are particularly challenging when they mimic the form of real GWs. Given the higher expected event rate in the current observing run (O4), LIGO-Virgo GW event candidate validation requires increased levels of automation. Gravity Spy, a machine learning tool that successfully classified common types of LIGO and Virgo glitches in previous observing runs, has the potential to be restructured as a signal-vs-glitch classifier to distinguish between glitches and GW signals accurately. A signal-vs-glitch classifier used for automation must be robust and compatible with a broad array of background noise, new sources of glitches, and the likely occurrence of overlapping glitches and GWs. This dissertation presents GSpyNetTree, the Gravity Spy Convolutional Neural Network Decision Tree: a multi-CNN multi-label classifier using CNNs in a decision tree sorted via total GW candidate mass. Integrated into the LIGO-Virgo Data Quality Report, GSpyNetTree is one of the essential tools in assessing the necessity of glitch mitigation in O4. This thesis presents the development, building process, and results of GSpyNetTree: from its origin as a multi-class classifier based on Gravity Spy, to its current O4 status as a multi-label classifier. Finally, the performance of GSpyNetTree identifying data quality issues in the public O4 GW candidates published in GraceDB is evaluated, and new ways to improve the tool's classifications are suggested.This material is based upon work supported by NSF's LIGO Laboratory, which is a major facility fully funded by the National Science Foundation. 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Livny, «Distributed computing in practice: the condor experience.», Concurrency - Practice and Experience 17, 323-356 (2005).201729031Publication88a1271b-7c5b-4cba-a02a-87878aba01e4virtual::9830-188a1271b-7c5b-4cba-a02a-87878aba01e4virtual::9830-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000382418virtual::9830-1ORIGINALPhysics Thesis GSpyNetTree Maria Sofia Alvarez Lopez.pdfPhysics Thesis GSpyNetTree Maria Sofia Alvarez Lopez.pdfTrabajo de grado final aprobadoapplication/pdf69922589https://repositorio.uniandes.edu.co/bitstreams/836d160d-195f-446e-ae86-4a42697f7cd4/download10905ac22b669a9e0c8d8b0a4c84993dMD54Autorizacion tesis Fisica Maria Sofia Alvarez Lopez 201729031.pdfAutorizacion tesis Fisica Maria Sofia Alvarez Lopez 201729031.pdfHIDEapplication/pdf342884https://repositorio.uniandes.edu.co/bitstreams/ae066aba-b5e1-42de-a20b-84bd4cf810b0/download624513f1a982d26714a4b817be5affcbMD53THUMBNAILPhysics Thesis GSpyNetTree Maria Sofia Alvarez Lopez.pdf.jpgPhysics Thesis GSpyNetTree Maria Sofia Alvarez Lopez.pdf.jpgIM Thumbnailimage/jpeg7402https://repositorio.uniandes.edu.co/bitstreams/6c4a26e5-13a7-4991-adac-6a9e905c9718/download19066e9311a8e5e7083438b5515eab42MD56Autorizacion tesis Fisica Maria Sofia Alvarez Lopez 201729031.pdf.jpgAutorizacion tesis Fisica Maria Sofia Alvarez Lopez 201729031.pdf.jpgIM Thumbnailimage/jpeg15583https://repositorio.uniandes.edu.co/bitstreams/3e746659-3b7a-49bd-9b9d-4126c961f5d8/downloadecc29d2c99760a1d924f2ae327646f55MD58CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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