{"id":16089,"date":"2017-07-26T09:12:52","date_gmt":"2017-07-26T13:12:52","guid":{"rendered":"http:\/\/www.astrofisicamas.cl\/?p=16089"},"modified":"2023-08-07T18:41:14","modified_gmt":"2023-08-07T18:41:14","slug":"nuestra-investigacion-un-clasificador-de-aprendizaje-de-maquinas-para-rr-lyrae-del-vvv-survey","status":"publish","type":"post","link":"https:\/\/astrofisicamas.cl\/en\/nuestra-investigacion-un-clasificador-de-aprendizaje-de-maquinas-para-rr-lyrae-del-vvv-survey\/","title":{"rendered":"Nuestra Investigaci\u00f3n: Un clasificador basado en aprendizaje de m\u00e1quinas para RR Lyrae del VVV Survey"},"content":{"rendered":"<h4><em><strong>Por Felipe Elorrieta L\u00f3pez, estudiante doctorado\u00a0MAS y del Departamento de Estad\u00edstica, Facultad de Matem\u00e1ticas UC\u00a0<\/strong><\/em><\/h4>\n<p><!--more--><\/p>\n<p><strong>Hist\u00f3ricamente las estrellas variables han sido la herramienta principal para determinar la estructura de los sistemas estelares.<\/strong> Entre ellas, se pueden distinguir dos tipos dependiendo de si el proceso que crea la variabilidad observada es inherente a la estrella (variaci\u00f3n intr\u00ednseca) o no (variaci\u00f3n extr\u00ednseca). Entre las variables intr\u00ednsecas est\u00e1n las <strong>estrellas pulsantes, las cuales contienen las clases RR Lyrae y Cepheidas, que satisfacen una relaci\u00f3n entre sus periodos y sus luminosidades absolutas, lo cual permite estimar las distancias.<\/strong><\/p>\n<p>El <a href=\"http:\/\/www.vvvsurvey.org\">VVV<\/a> (Vista Variables in the V\u00eda L\u00e1ctea) es un sondeo p\u00fablico de la <a href=\"http:\/\/www.eso.org\">ESO<\/a> (European Southern Observatory) que utiliza el telescopio VISTA, instalado en el Observatorio Paranal, <strong>cuyo principal objetivo es la creaci\u00f3n de un mapa tridimensional preciso del bulbo gal\u00e1ctico. <\/strong>Para ello, resulta necesario identificar las estrellas pulsantes.<\/p>\n<p>En el \u00e1rea que cubre el VVV se espera encontrar entre 10^6-10^7 \u00a0estrellas variables, entre muchos m\u00e1s millones de estrellas que no var\u00edan. <strong>Debido al gran n\u00famero de fuentes esperadas se requieren mecanismos automatizados para analizar la informaci\u00f3n disponible sobre el brillo estelar observado a lo largo del tiempo (que llamamos \u201ccurva de luz\u201d). Aqu\u00ed, las herramientas estad\u00edsticas se vuelven esenciales.<\/strong><\/p>\n<p><strong>Debido a esto, <\/strong><a href=\"https:\/\/arxiv.org\/abs\/1610.05707\"><strong>creamos un clasificador supervisado<\/strong><\/a><strong> para asignar un \u201cscore\u00bb a curvas de luz de la banda Ks del VVV (que mide una regi\u00f3n infrarroja del espectro). En una primera etapa, este clasificador indica la probabilidad de que una estrella sea una RR-Lyrae de una clase espec\u00edfica llamada \u201ctipo ab\u201d (RRab).<\/strong><\/p>\n<p>Para la construcci\u00f3n del clasificador se extraen un conjunto de 68 caracter\u00edsticas a partir de las curvas de luz de cada estrella identificada como variable y de los par\u00e1metros del modelo arm\u00f3nico con el que son ajustadas. Estas caracter\u00edsticas servir\u00e1n como datos de entrada para el clasificador.<\/p>\n<p>Como el VVV observa en el infrarrojo cercano (NIR), la creaci\u00f3n de un clasificador aqu\u00ed ofrece desaf\u00edos adicionales en comparaci\u00f3n con el \u00f3ptico, ya que a\u00fan son escasas las curvas de luz NIR de alta calidad con las que se pueden entrenar los clasificadores supervisados. En la figura se muestra un ejemplo de las curvas de luz observadas en el \u00f3ptico e infrarrojo para una estrella RRab conocida, donde se nota una curva de luz muy sim\u00e9trica en el infrarrojo (panel inferior).<\/p>\n<p><a href=\"http:\/\/www.astrofisicamas.cl\/wp-content\/uploads\/2017\/07\/2017_07_26_FElorrieta.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-16090 size-medium\" src=\"http:\/\/www.astrofisicamas.cl\/wp-content\/uploads\/2017\/07\/2017_07_26_FElorrieta-300x235.png\" alt=\"\" width=\"300\" height=\"235\" \/><\/a><\/p>\n<p>De las caracter\u00edsticas iniciales, se determin\u00f3 que es suficiente el uso de 12 de ellas para obtener el mejor rendimiento final. La caracter\u00edstica m\u00e1s importante fue el per\u00edodo, lo cual era esperable debido a que est\u00e1 muy bien definido el rango de per\u00edodos de las estrellas RRab. El clasificador elegido \u2013el algoritmo AdaBoost.M1 (Freund et al., 1996)\u2013 alcanza buenas medidas de rendimiento tanto en la validaci\u00f3n cruzada de los conjuntos de entrenamiento, como en la comparaci\u00f3n del desempe\u00f1o respecto a dos conjuntos de datos clasificados por expertos.<\/p>\n<p>Imagen principal: Estrellas variables RR Lyrae VVV Survey<\/p>\n<p>Cr\u00e9dito:\u00a0ESO\/VVV Survey\/D. Minniti<\/p>","protected":false},"excerpt":{"rendered":"<p>Por Felipe Elorrieta L\u00f3pez, estudiante doctorado\u00a0MAS y del Departamento de Estad\u00edstica, Facultad de Matem\u00e1ticas UC\u00a0<\/p>","protected":false},"author":3,"featured_media":29237,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_eb_attr":"","footnotes":""},"categories":[59],"tags":[],"class_list":["post-16089","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-columnas"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Nuestra Investigaci\u00f3n: Un clasificador basado en aprendizaje de m\u00e1quinas para RR Lyrae del VVV Survey - Instituto Milenio de Astrof\u00edsica MAS<\/title>\n<meta name=\"robots\" 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