Journal of Computational Astronomy & Astronomical Computing (JCAAC)

JAAC 2, 61-74 (2025)

Section: Herramientas del observatorio virtual

Caracterización de cúmulos estelares abiertos

 Joaquín Álvaro Contreras, FAAE, Madrid, Spain
 jalvaro@citelan.es

Resumen
El problema de discriminar qué estrellas forman parte de un cúmulo estelar abierto, en adelante OC (Open Cluster), descartando aquellas que no son parte del cúmulo, es fundamental para determinar la existencia de un OC, y en definitiva caracterizarlo, atribuyéndole una estimación precisa de sus componentes y a partir de estos, la masa del cúmulo, edad, distancia, movimiento propio relativo en la galaxia, composición química, etc. El proceso de caracterizar un OC no es fácil y, aunque ya se utilizan técnicas de inteligencia artificial eficaces, conocer los fundamentos y tareas para resolver este problema es conveniente si se desea trabajar en este campo, ya sea para refinar datos de OCs conocidos como para descubrir otros que aún no lo son. Afortunadamente hay algunas herramientas del Observatorio Virtual (VO) que facilitan la tarea. En este artículo, y los siguientes de esta serie, haremos un repaso amplio sobre algunos casos prácticos utilizando herramientas VO como TOPCAT, Aladin, Clusterix 2.0 y VOSA, e intentaremos finalmente un desarrollo con técnicas de inteligencia artificial con este mismo propósito.

Abstract
The problem of identifying which stars belong to an open star cluster (hereinafter OC, Open Cluster), while discarding those that do not, is fundamental to determining the existence of an OC and, ultimately, characterizing it. This involves providing an accurate estimate of its members and, based on them, calculating properties such as the cluster’s mass, age, distance, relative proper motion within the galaxy, chemical composition, and more. The process of characterizing an OC is not straightforward, and although effective artificial intelligence techniques are already in use, understanding the fundamentals and tasks required to address this problem is essential for those who wish to work in this field, whether to refine data for known OCs or to discover previously unidentified ones. Fortunately, there are tools from the Virtual Observatory (VO) that make this task easier. In this article, and in subsequent ones in this series, we will provide a comprehensive review of practical cases using VO tools such as TOPCAT, Aladin, Clusterix 2.0 and VOSA, and we will conclude by attempting a development using artificial intelligence techniques for the same purpose.

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The Journal of Computational Astronomy & Astronomical Computing is an effort by the FAAE - Grupo de Cálculo Astronómico (GCA) to encourage the use of software tools and the development of codes and algorithms for astronomical applications within the framework of amateur astronomy, as well as to connect the amateur community with the professional astronomy community and promote cross-collaboration and ProAm projects between both groups.