Diagnostic tool to analyse colour–magnitude diagrams of poorly populated stellar concentrations

• Autor(a) principal: Pavani, Daniela Borges
• Data de Publicação: 2011
• Outros Autores: Kerber, Leandro de Oliveira, Bica, Eduardo Luiz Damiani, Maciel, Walter Junqueira
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/96279
• topic: Aglomerados abertos e associações; Aglomerados estelares; Diagramas; Methods: data analysis; Methods: statistical; Open clusters and associations: individual: DoDz 6; Open clusters and associations: individual: NGC272; Open clusters and associations: individual: ESO 435 SC48; Open clusters and associations: individual: ESO 325 SC15

The dynamical processes that lead to open cluster disruption cause its mass to decrease. To investigate such processes from the observational point of view, it is important to identify open cluster remnants (OCRs), which are intrinsically poorly populated. Due to their nature, distinguishing them from field star fluctuations is still an unresolved issue. In this work, we developed a statistical diagnostic tool to distinguish poorly populated star concentrations from background field fluctuations. We use 2MASS photometry to explore one of the conditions required for a stellar group to be a physical group: to produce distinct sequences in a colour– magnitude diagram (CMD). We use automated tools to (i) derive the limiting radius; (ii) decontaminate the field and assign membership probabilities; (iii) fit isochrones; and (iv) compare object and field CMDs, considering the isochrone solution, in order to verify the similarity. If the object cannot be statistically considered as a field fluctuation, we derive its probable age, distance modulus, reddening and uncertainties in a self-consistent way. As a test, we apply the tool to open clusters and comparison fields. Finally, we study the OCR candidates DoDz 6,NGC272, ESO 435 SC48 and ESO 325 SC15. The tool is optimized to treat these lowstatistic objects and to separate the bestOCRcandidates for studies on kinematics and chemical composition. The study of the possible OCRs will certainly provide a deep understanding of OCR properties and constraints for theoretical models, including insights into the evolution of open clusters and dissolution rates.