Astroinformatics-based search for globular clusters in the Fornax Deep Survey
Date Issued
2019
Author(s)
Angora, G.
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Longo, G.
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Capaccioli, M.
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D'Abrusco, R.
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D'Ago, G.
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Hilker, M.
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Mieske, S.
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Peletier, R.
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Pota, V.
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Puzia, T.
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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.
Volume
490
Issue
3
Start page
4080
Issn Identifier
0035-8711
Ads BibCode
2019MNRAS.490.4080A
Rights
open.access
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