Using the PLC and modern algorithms to detect clustering and associations in nearby galaxies
The Hubble Space Telescope (HST) is not affected by the atmospheric seeing, bringing observations that have an amazing spatial resolution that allows us to isolate the individual stars in nearby galaxies. In consequence, to have good photometry of large samples of stars in these objects. Therefore,...
| Autores principales: | , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/155357 http://www.aspbooks.org/publications/522/549.pdf |
| Aporte de: |
| Sumario: | The Hubble Space Telescope (HST) is not affected by the atmospheric seeing, bringing observations that have an amazing spatial resolution that allows us to isolate the individual stars in nearby galaxies. In consequence, to have good photometry of large samples of stars in these objects. Therefore, high accuracy studies of extragalactic stellar associations and clusters could be done. One of most powerful algorithm for detecting clustering in a large amount of data is the Path Linkage Criterion (PLC), Battinelli (1991). We show in this work the results of our implementation of a high speed version of the PLC that was applied to HST data of two galaxies: NGC 300 and NGC 253. Also, we show the results obtained with PLC and others popular methods found in the literature of clustering, applied to the real data and to simulated data. |
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