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http://hdl.handle.net/2183/34054 Exploración de técnicas de IA en entornos Big Data para la estimación de parámetros físicos estelares mediante espectros RVS de la misión espacial Gaia
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D’Angelo Sabin, Luca
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Universidade da Coruña. Facultade de Informática
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Abstract
[Resumen] El satélite Gaia de la Agencia Espacial Europea recoge mediciones de miles de millones de
estrellas como parte de su misión, apodada con el mismo nombre. Entre los instrumentos utilizados
en sus mediciones está el Espectrómetro de Velocidad Radial (RVS), que recaba los
espectros de luz emitidos por las estrellas, y de los cuales se puede obtener información valiosa
sobre la composición y parámetros físicos de la misma. Dichos espectros son complejos de
interpretar, por lo que, incluso desde antes de la puesta en órbita del satélite, se analizaron diferentes
alternativas con sistemas inteligentes para poder procesar y extraer esa información
de millones de espectros.
Recientemente, los primeros espectros RVS de Gaia se hicieron públicos en la Gaia Data
Release 3 del 13 de junio del 2022, por lo que es la oportunidad perfecta para explorar técnicas
de la Inteligencia Artificial sobre los datos observacionales obtenidos del satélite en búsqueda
de modelos que puedan representar esos datos con máxima precisión. También se realizará
un análisis exhaustivo de estos datos para detectar posibles problemas que puedan presentar
debido al ruido inherente, cuyo modelo no ha sido precisado.
[Abstract] The European Space Agency’s Gaia satellite collects information from billions of stars as part of its mission, which shares the same name. Among the various instruments used in its measurements is the Radial Velocity Spectrometer (RVS), which gathers the light spectra emitted by stars, from which valuable information about their composition and physical parameters can be obtained. These spectra are complex to interpret, so even before the satellite had been launched, different alternatives with intelligent systems were analyzed with the goal of processing and extracting information from millions of spectra. Recently, the first RVS spectra from Gaia were made public in the Gaia Data Release 3 on June 13, 2022. This presents the perfect opportunity to explore Artificial Intelligence techniques on the observational data obtained from the satellite in search of models that can represent this data with the highest precision. An exhaustive analysis of this data will also be conducted to detect possible issues that may arise due to inherent noise, which has not been precisely modelled yet.
[Abstract] The European Space Agency’s Gaia satellite collects information from billions of stars as part of its mission, which shares the same name. Among the various instruments used in its measurements is the Radial Velocity Spectrometer (RVS), which gathers the light spectra emitted by stars, from which valuable information about their composition and physical parameters can be obtained. These spectra are complex to interpret, so even before the satellite had been launched, different alternatives with intelligent systems were analyzed with the goal of processing and extracting information from millions of spectra. Recently, the first RVS spectra from Gaia were made public in the Gaia Data Release 3 on June 13, 2022. This presents the perfect opportunity to explore Artificial Intelligence techniques on the observational data obtained from the satellite in search of models that can represent this data with the highest precision. An exhaustive analysis of this data will also be conducted to detect possible issues that may arise due to inherent noise, which has not been precisely modelled yet.
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Atribución 3.0 España







