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Astroinformatics based search for globular clusters in the Fornax Deep Survey

Angora, G. and Brescia, M. and Cavuoti, S. and Paolillo, M. and Longo, G. and Cantiello, M. and Capaccioli, M. and D’Abrusco, R. and D’Ago, G. and Hilker, M. and Iodice, E. and Mieske, S. and Napolitano, N. and Peletier, R. and Pota, V. and Puzia, T. and Riccio, G. and Spavone, M. (2019) Astroinformatics based search for globular clusters in the Fornax Deep Survey. Monthly Notices of the Royal Astronomical Society, 490 (3). pp. 4080-4106. ISSN 0035-8711. https://resolver.caltech.edu/CaltechAUTHORS:20200109-143244940

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/stz2801DOIArticle
https://arxiv.org/abs/1910.01884arXivDiscussion Paper
ORCID:
AuthorORCID
Angora, G.0000-0002-0316-6562
Brescia, M.0000-0001-9506-5680
Cavuoti, S.0000-0002-3787-4196
Paolillo, M.0000-0003-4210-7693
Longo, G.0000-0002-9182-8414
Cantiello, M.0000-0002-8171-8596
Puzia, T.0000-0003-0350-7061
Riccio, G.0000-0001-7020-1172
Spavone, M.0000-0002-6427-7039
Additional Information:© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Accepted 2019 September 30. Received 2019 September 28; in original form 2019 May 7. The authors thank the anonymous referee for all very helpful comments and suggestions that improved the scientific quality of the presented work. MB acknowledges the INAF Progetto di Ricerca di Interesse Nazionale - Square Kilometer Array (PRIN-SKA) 2017 program 1.05.01.88.04 and the funding from MIUR Premiale 2016: Mining the Cosmos - Big Data and Innovative italian technology for Frontier Astrofhysics and Cosmology (MITIC). MP acknowledges support from Progetto di Ricerca di Interesse Nazionale (PRIN) INAF 2014 ‘Fornax Cluster Imaging and Spectroscopic Deep Survey’. MP and SC acknowledge support from the project ‘Quasars at high redshift: physics and cosmology’ financed by the ASI/INAF agreement 2017-14-H.0. GL, RP, and NN acknowledge support from the European Union’s Horizon 2020 Sundial Innovative Training Network, grant no. 721463. NN acknowledges support from the 100 Top Talent Program of the Sun Yat-sen University, Guandong Province. MS and EI acknowledge financial support from the VST project. RD’A is supported by NASA contract NAS8-03060 (Chandra X-ray Center). GD acknowledges support from Comisión Nacional de Investigación Cientifica y Tecnológica (CONICYT) project Basal AFB-170002. DAMEWARE has been used for ML experiments (Brescia et al. 2014). Topcat has been used for this work (Taylor 2005). C3 has been used for efficient catalogue cross-matching (Riccio et al. 2017).
Funders:
Funding AgencyGrant Number
Istituto Nazionale di Astrofisica (INAF)1.05.01.88.04
Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR)UNSPECIFIED
Agenzia Spaziale Italiana (ASI)2017-14-H.0
Marie Curie Fellowship721463
100 Top Talent Program of the Sun Yat-sen UniversityUNSPECIFIED
VLT Survey TelescopeUNSPECIFIED
NASANAS8-03060
BASAL-CATAAFB-170002
Subject Keywords:methods: data analysis, globular clusters: general, galaxies: elliptical and lenticular, cD
Issue or Number:3
Record Number:CaltechAUTHORS:20200109-143244940
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200109-143244940
Official Citation:G Angora, M Brescia, S Cavuoti, M Paolillo, G Longo, M Cantiello, M Capaccioli, R D’Abrusco, G D’Ago, M Hilker, E Iodice, S Mieske, N Napolitano, R Peletier, V Pota, T Puzia, G Riccio, M Spavone, Astroinformatics-based search for globular clusters in the Fornax Deep Survey, Monthly Notices of the Royal Astronomical Society, Volume 490, Issue 3, December 2019, Pages 4080–4106, https://doi.org/10.1093/mnras/stz2801
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100618
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:10 Jan 2020 16:06
Last Modified:10 Jan 2020 16:06

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