Searching for extragalactic variable stars using machine learning algorithms

With the advent of the digital era, production of astronomy data has grown exponentially. However, traditional methods of searching for variable stars become ineffective when dealing with these amounts of data. Therefore, it is necessary to explore new techniques to automatise the search and have a trustworthy classification of variable stars. The objective of this work is to find variable stars in the galaxy NGC 55 using public wide field images taken for the Araucaria Project. The 29 images were taken between 2003 and 2006 using the 2.2m MPG/ESO Telescope at La Silla observatory in Chile. It is worth noting that the images are not used in any publication on NGC 55, making this work an independent study of the galaxy. We developed a pipeline using IRAF and Astropy in order to process the images and to correct their World Coordinate System. Additionally, a final stack was made for 23 of the nights...

• Autores: Acevedo Barroso, Javier Alejandro
• Fecha de publicación: 2020
• Institución: Universidad de los Andes
• Repositorio: Séneca: repositorio Uniandes
• Idioma: eng
• OAI Identifier: oai:repositorio.uniandes.edu.co:1992/51684
• Acceso en línea: http://hdl.handle.net/1992/51684
• Palabra clave: Estrellas variables; Fotometría; Aprendizaje automático (Inteligencia artificial); Física
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/