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http://hdl.handle.net/20.500.12386/28782
Titolo: | Astroinformatics-based search for globular clusters in the Fornax Deep Survey | Autori: | Angora, G. BRESCIA, Massimo CAVUOTI, STEFANO Paolillo, Maurizio Longo, G. CANTIELLO, Michele Capaccioli, M. D'Abrusco, R. D'Ago, G. Hilker, M. IODICE, ENRICHETTA Mieske, S. NAPOLITANO, NICOLA ROSARIO Peletier, R. Pota, V. Puzia, T. RICCIO, GIUSEPPE SPAVONE, MARILENA |
Data pubblicazione: | 2019 | Rivista: | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY | Numero: | 490 | Fascicolo: | 3 | Da pagina:: | 4080 | Abstract: | In the last years, Astroinformatics has become a well-defined paradigm for many fields of Astronomy. In this work, we demonstrate the potential of a multidisciplinary approach to identify globular clusters (GCs) in the Fornax cluster of galaxies taking advantage of multiband photometry produced by the VLT Survey Telescope using automatic self-adaptive methodologies. The data analysed in this work consist of deep, multiband, partially overlapping images centred on the core of the Fornax cluster. In this work, we use a Neural Gas model, a pure clustering machine learning methodology, to approach the GC detection, while a novel feature selection method (ΦLAB) is exploited to perform the parameter space analysis and optimization. We demonstrate that the use of an Astroinformatics-based methodology is able to provide GC samples that are comparable, in terms of purity and completeness with those obtained using single-band HST data and two approaches based, respectively, on a morpho-photometric and a Principal Component Analysis using the same data discussed in this work. | URI: | http://hdl.handle.net/20.500.12386/28782 | URL: | https://academic.oup.com/mnras/article/490/3/4080/5583072 | ISSN: | 0035-8711 | DOI: | 10.1093/mnras/stz2801 | Bibcode ADS: | 2019MNRAS.490.4080A | Fulltext: | open |
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