Astroinformatics-based search for globular clusters in the Fornax Deep Survey

• Published in: Monthly notices of the Royal Astronomical Society 2019-12, Vol.490 (3), p.4080-4106
• Main Authors: Angora, G, Brescia, M, Cavuoti, S, Paolillo, M, Longo, G, Cantiello, M, Capaccioli, M, D’Abrusco, R, D’Ago, G, Hilker, M, Iodice, E, Mieske, S, Napolitano, N, Peletier, R, Pota, V, Puzia, T, Riccio, G, Spavone, M
• Format: Article
• Language: English
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stz2801

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.