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

2019/10/04 by Giuseppe Angora, G. Angora, Massimo Brescia +29 · 9 citations
Environmental Science · Mathematics · Physics and Astronomy · #Advanced Statistical Methods and Models #Artificial intelligence #Astrophysics #Cluster (spacecraft) #Cluster analysis #Computer science #Data mining #Data reduction #Fornax Cluster #Galaxies: Formation, Evolution, Phenomena #Galaxy #Galaxy cluster #Globular cluster #Photometry (optics) #Physics #Remote Sensing in Agriculture #Stars #astro-ph.IM

paper · pdf · doi:10.1093/mnras/stz2801

published in Monthly Notices of the Royal Astronomical Society 490(3), 4080-4106 (Oxford University Press) · 29 pages, 14 figures

arxiv created 2019/10/04 · openalex publication_date 2019/10/04 · arxiv updated 2019/10/15 · openalex created_date 2019/10/18 · openalex updated_date 2026/08/05

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.

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