Astroinformatics-based search for globular clusters in the Fornax Deep Survey. Issue 3 (7th October 2019)

• Record Type: Journal Article
• 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
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 490:Issue 3(2019)
• Page Start: 4080
• Page End: 4106
• Publication Date: 2019-10-07
• Subjects: methods: data analysis -- globular clusters: general -- galaxies: elliptical and lenticular, cD; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stz2801
• Languages: English
• ISSNs: 0035-8711

Abstract

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.