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

dc.contributorGarcía Varela, José Alejandro
dc.contributorMcIver, Jess
dc.contributorChan, Man Leong (Mervyn)
dc.contributorAstronomía y astrofísica
dc.creatorÁlvarez López, María Sofía
dc.date.accessioned2023-07-25T18:20:57Z
dc.date.accessioned2023-09-07T00:53:34Z
dc.date.available2023-07-25T18:20:57Z
dc.date.available2023-09-07T00:53:34Z
dc.date.created2023-07-25T18:20:57Z
dc.date.issued2023-07-12
dc.identifierhttp://hdl.handle.net/1992/68738
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8727927
dc.description.abstractA 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.
dc.description.abstractDespite 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.
dc.languageeng
dc.publisherUniversidad de los Andes
dc.publisherFísica
dc.publisherFacultad de Ciencias
dc.publisherDepartamento de Física
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dc.rightsAttribution-NoDerivatives 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleImproving our ability to distinguish gravitational-wave signals from detector transient noise for the fourth LIGO-Virgo-KAGRA observing run
dc.typeTrabajo de grado - Pregrado


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